This document summarizes research using a SIMD array processor and neural networks for medical image processing. It discusses three key developments: 1) A parallel implementation of the "snake" algorithm extended to closed contours for interpreting medical images, 2) A software interface providing interactive or autonomous control of the algorithm from workstations, 3) Using the array processor as a compute server for conventional image processing algorithms. It then reviews applications of neural networks in medical image pre-processing, segmentation, and object detection/recognition, discussing advantages and limitations of different neural network approaches.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
SWARM OPTIMIZED MODULAR NEURAL NETWORK BASED DIAGNOSTIC SYSTEM FOR BREAST CAN...ijscai
The document describes a modular neural network approach optimized by particle swarm optimization for breast cancer diagnosis. The approach uses a modular neural network with several independent neural network experts that analyze input data individually and provide outputs that are combined by an integrator. Particle swarm optimization is used to determine optimal connections for each expert neural network during training. The optimized modular neural network is then used to classify breast cancer samples as cancerous or non-cancerous, demonstrating better diagnostic ability than traditional methods.
Review of Classification algorithms for Brain MRI imagesIRJET Journal
1) The document reviews various classification algorithms that have been used to classify brain MRI images as normal or abnormal. It discusses techniques like decision trees, neural networks, fuzzy logic, and clustering that have been applied.
2) It provides examples of several studies that first performed preprocessing tasks like feature extraction on MRI images before applying classification algorithms like naive Bayes, decision trees, and probabilistic neural networks to classify images with accuracies ranging from 88% to 100%.
3) Boosting and ensemble techniques like combining multiple weak learners into a strong learner are mentioned as ways to improve classification accuracy and response times. The document concludes by surveying different algorithms and their performance on classifying brain tumor MRI images.
Techniques of Brain Cancer Detection from MRI using Machine LearningIRJET Journal
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document summarizes a research paper that proposes a technique for classifying brain CT scan images using principal component analysis (PCA), wavelet transform, and K-nearest neighbors (K-NN) classification. The methodology involves extracting features from CT scan images using PCA and wavelet transform, then training a K-NN classifier on the extracted features to classify images as normal or abnormal. PCA achieved 100% accuracy on brain CT scans, while wavelet transform achieved 100% accuracy on Brodatz texture images. The technique provides an automated way to analyze CT scans and could help radiologists in diagnosis.
Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...Dr.NAGARAJAN. S
Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
SWARM OPTIMIZED MODULAR NEURAL NETWORK BASED DIAGNOSTIC SYSTEM FOR BREAST CAN...ijscai
The document describes a modular neural network approach optimized by particle swarm optimization for breast cancer diagnosis. The approach uses a modular neural network with several independent neural network experts that analyze input data individually and provide outputs that are combined by an integrator. Particle swarm optimization is used to determine optimal connections for each expert neural network during training. The optimized modular neural network is then used to classify breast cancer samples as cancerous or non-cancerous, demonstrating better diagnostic ability than traditional methods.
Review of Classification algorithms for Brain MRI imagesIRJET Journal
1) The document reviews various classification algorithms that have been used to classify brain MRI images as normal or abnormal. It discusses techniques like decision trees, neural networks, fuzzy logic, and clustering that have been applied.
2) It provides examples of several studies that first performed preprocessing tasks like feature extraction on MRI images before applying classification algorithms like naive Bayes, decision trees, and probabilistic neural networks to classify images with accuracies ranging from 88% to 100%.
3) Boosting and ensemble techniques like combining multiple weak learners into a strong learner are mentioned as ways to improve classification accuracy and response times. The document concludes by surveying different algorithms and their performance on classifying brain tumor MRI images.
Techniques of Brain Cancer Detection from MRI using Machine LearningIRJET Journal
The document discusses techniques for detecting brain cancer from MRI scans using machine learning. It first provides background on brain tumors and MRI. It then outlines the cancer detection process, including pre-processing the MRI data, segmenting the images, extracting features, and classifying tumors using techniques like CNNs, SVMs, MLP, and Naive Bayes. The document reviews related work applying these techniques and compares their results, finding accuracy can be improved with larger, higher resolution datasets.
The document discusses using a U-Net convolutional neural network to automatically segment brain tumors in MRI images. It aims to eliminate the need for domain expertise by using deep learning to extract hierarchical features. The U-Net model is trained on the BRATS 2017 dataset and is able to segment tumors with 5% higher accuracy than previous methods, as measured by the Dice similarity coefficient. The system could be expanded to analyze additional MRI modalities and further improve automated tumor detection.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This document summarizes a research paper that proposes a technique for classifying brain CT scan images using principal component analysis (PCA), wavelet transform, and K-nearest neighbors (K-NN) classification. The methodology involves extracting features from CT scan images using PCA and wavelet transform, then training a K-NN classifier on the extracted features to classify images as normal or abnormal. PCA achieved 100% accuracy on brain CT scans, while wavelet transform achieved 100% accuracy on Brodatz texture images. The technique provides an automated way to analyze CT scans and could help radiologists in diagnosis.
Hybrid Pixel-Based Method for Multimodal Medical Image Fusion Based on Integr...Dr.NAGARAJAN. S
Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Brain Tumor Detection and Classification using Adaptive BoostingIRJET Journal
1. The document describes a system for detecting and classifying brain tumors using MRI images.
2. The system uses techniques like preprocessing, segmentation using k-means clustering, feature extraction with discrete wavelet transform and principal component analysis for dimension reduction, and classification with decision trees and adaptive boosting.
3. Adaptive boosting combines multiple weak learners or decision trees into a strong classifier and focuses on misclassified examples to improve accuracy, achieving 100% accuracy for tumor detection and classification in the system.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes recent work on content-based image retrieval (CBIR) techniques for medical images. It discusses several methods used for CBIR, including shape-based, texture-based, and feature selection methods. Recent CBIR works are surveyed that use approaches like support vector machines, nearest neighbor algorithms, and relevance feedback. While progress has been made, the document notes there are still research gaps around bridging the semantic gap between low-level image features and high-level concepts, and improving retrieval accuracy and efficiency.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
IRJET-A Review on Brain Tumor Detection using BFCFCM AlgorithmIRJET Journal
The document presents a review of brain tumor detection using the BFCFCM clustering algorithm. It begins with an introduction to brain tumors and MRI imaging. It then reviews several existing techniques for brain tumor detection using artificial neural networks, linear discriminant analysis, neuro-fuzzy systems, and region growing segmentation with watershed algorithms. The document proposes a method using pre-processing, skull masking, segmentation with an advanced fuzzy c-means algorithm, feature extraction through thresholding, and an SVM classifier. Segmentation partitions the MRI image into regions/objects of interest like the tumor. Feature extraction analyzes the segmented regions to characterize the tumor for classification.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
This document proposes a method for detecting brain tumors from MRI images using binary image processing and k-means clustering. MRI images are first converted to binary images using morphological filtering. This allows for more efficient hardware implementation of image processing operations like dilation and erosion. The binary images then undergo k-means clustering to segment and detect the tumor region. Simulation results show the tumor was successfully detected in binary images processed with morphological filtering and k-means clustering. The proposed method aims to reduce computational complexity and hardware requirements for brain tumor detection compared to existing methods.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
A New Algorithm for Fully Automatic Brain Tumor Segmentation with 3-D Convolu...Christopher Mehdi Elamri
This document describes a new algorithm for fully automatic brain tumor segmentation using 3D convolutional neural networks. The algorithm uses 3D convolutional filters to preserve spatial information, and a high-bias CNN architecture to increase effective data size and reduce model variance. On a dataset of 274 brain MR images, the algorithm achieved a median Dice score of 89% for whole tumor segmentation, significantly outperforming past methods. This demonstrates the effectiveness of generalizing low-bias high-variance methods like CNNs to learn from medium-sized datasets.
Medical image enhancement using histogram processing part2Prashant Sharma
This document discusses techniques for enhancing medical images using histogram processing. It introduces Brightness Preserving Bi-Histogram Equalization (BBHE) and Dualistic Sub-image Histogram Equalization (DSIHE), which partition an image histogram into two parts based on mean or median grayscale levels, respectively, and independently equalize the sub-images. It provides the mathematical formulations and algorithms for BBHE and DSIHE, and compares their performance on medical images using metrics like absolute mean brightness error, maximum difference, and peak signal-to-noise ratio. The document concludes by stating BBHE and DSIHE can improve low contrast in medical images for better diagnosis.
Medical Image Processing in Nuclear Medicine and Bone ArthroplastyIOSR Journals
This document discusses medical image processing in nuclear medicine and bone arthroplasty. It provides background on nuclear medicine imaging techniques like planar imaging, SPECT, PET and hybrid SPECT/CT and PET/CT systems. It then discusses how MATLAB can be used for medical image processing tasks in nuclear medicine like organ contouring, interpolation, filtering, segmentation, background removal, registration and volume quantification. Specific examples of nuclear medicine examinations that can be analyzed using MATLAB algorithms are also mentioned.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
IRJET - Detection of Heamorrhage in Brain using Deep LearningIRJET Journal
This document presents a method for detecting hemorrhage in brain CT scans using deep learning. It begins with an introduction to brain hemorrhage and the need for automated detection. Previous related work using various segmentation and classification methods is summarized. Deep learning is identified as a promising technique due to its ability to extract complex features from images. The proposed method uses a convolutional neural network model with several convolutional, max pooling, dropout and dense layers to classify brain CT scans as either normal or hemorrhagic. The model is trained on 180 images and tested on 20 images, achieving an accuracy of 94.4% at predicting hemorrhage. The method provides a fast and automated way to detect hemorrhage in brain CT scans to help
Brain Tumor Detection and Classification using Adaptive BoostingIRJET Journal
1. The document describes a system for detecting and classifying brain tumors using MRI images.
2. The system uses techniques like preprocessing, segmentation using k-means clustering, feature extraction with discrete wavelet transform and principal component analysis for dimension reduction, and classification with decision trees and adaptive boosting.
3. Adaptive boosting combines multiple weak learners or decision trees into a strong classifier and focuses on misclassified examples to improve accuracy, achieving 100% accuracy for tumor detection and classification in the system.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes recent work on content-based image retrieval (CBIR) techniques for medical images. It discusses several methods used for CBIR, including shape-based, texture-based, and feature selection methods. Recent CBIR works are surveyed that use approaches like support vector machines, nearest neighbor algorithms, and relevance feedback. While progress has been made, the document notes there are still research gaps around bridging the semantic gap between low-level image features and high-level concepts, and improving retrieval accuracy and efficiency.
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
IRJET-A Review on Brain Tumor Detection using BFCFCM AlgorithmIRJET Journal
The document presents a review of brain tumor detection using the BFCFCM clustering algorithm. It begins with an introduction to brain tumors and MRI imaging. It then reviews several existing techniques for brain tumor detection using artificial neural networks, linear discriminant analysis, neuro-fuzzy systems, and region growing segmentation with watershed algorithms. The document proposes a method using pre-processing, skull masking, segmentation with an advanced fuzzy c-means algorithm, feature extraction through thresholding, and an SVM classifier. Segmentation partitions the MRI image into regions/objects of interest like the tumor. Feature extraction analyzes the segmented regions to characterize the tumor for classification.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
Image Binarization for the uses of Preprocessing to Detect Brain Abnormality ...Journal For Research
Computerized MR of brain image binarization for the uses of preprocessing of features extraction and brain abnormality identification of brain has been described. Binarization is used as intermediate steps of many MR of brain normal and abnormal tissues detection. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to the extensive black background or the large variation in contrast between background and foreground of MRI. Proposed binarization determines a threshold value using mean, variance, standard deviation and entropy followed by a non-gamut enhancement that can overcome the binarization problem. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error.
A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EEG SIGNAL CLASSIFICATIONsipij
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector
machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM
have been compared. This comparison was conducted to seek a robust method that would produce good
classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG)
signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder
with SVM has been proposed. The EEG dataset used in this research was created by the University of
Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature
engineering. However, our prosed method of autoencoder in combination with SVM produced a similar
accuracy of 65% without using any feature engineering technique. This research shows that this system of
classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally
control a robotic device or an exoskeleton.
This document proposes a method for detecting brain tumors from MRI images using binary image processing and k-means clustering. MRI images are first converted to binary images using morphological filtering. This allows for more efficient hardware implementation of image processing operations like dilation and erosion. The binary images then undergo k-means clustering to segment and detect the tumor region. Simulation results show the tumor was successfully detected in binary images processed with morphological filtering and k-means clustering. The proposed method aims to reduce computational complexity and hardware requirements for brain tumor detection compared to existing methods.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
An Ameliorate Technique for Brain Lumps Detection Using Fuzzy C-Means ClusteringIRJET Journal
This document discusses using fuzzy C-means clustering to improve detection of brain tumors in abnormal MRI images. It begins with an abstract that outlines using fuzzy clustering with local information to improve segmentation efficiency over other clustering methods. It then provides background on the importance of accurate brain tumor detection and challenges with current visual examination methods. The document proposes using a fuzzy level set algorithm for medical image segmentation and evaluation of the proposed method. It reviews various existing segmentation techniques and challenges, and suggests an improved technique using modified classifiers, feedback, and analyzing texture and shape properties with fuzzy C-means clustering for brain tumor detection and image retrieval from MRI data.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
A New Algorithm for Fully Automatic Brain Tumor Segmentation with 3-D Convolu...Christopher Mehdi Elamri
This document describes a new algorithm for fully automatic brain tumor segmentation using 3D convolutional neural networks. The algorithm uses 3D convolutional filters to preserve spatial information, and a high-bias CNN architecture to increase effective data size and reduce model variance. On a dataset of 274 brain MR images, the algorithm achieved a median Dice score of 89% for whole tumor segmentation, significantly outperforming past methods. This demonstrates the effectiveness of generalizing low-bias high-variance methods like CNNs to learn from medium-sized datasets.
Medical image enhancement using histogram processing part2Prashant Sharma
This document discusses techniques for enhancing medical images using histogram processing. It introduces Brightness Preserving Bi-Histogram Equalization (BBHE) and Dualistic Sub-image Histogram Equalization (DSIHE), which partition an image histogram into two parts based on mean or median grayscale levels, respectively, and independently equalize the sub-images. It provides the mathematical formulations and algorithms for BBHE and DSIHE, and compares their performance on medical images using metrics like absolute mean brightness error, maximum difference, and peak signal-to-noise ratio. The document concludes by stating BBHE and DSIHE can improve low contrast in medical images for better diagnosis.
Medical Image Processing in Nuclear Medicine and Bone ArthroplastyIOSR Journals
This document discusses medical image processing in nuclear medicine and bone arthroplasty. It provides background on nuclear medicine imaging techniques like planar imaging, SPECT, PET and hybrid SPECT/CT and PET/CT systems. It then discusses how MATLAB can be used for medical image processing tasks in nuclear medicine like organ contouring, interpolation, filtering, segmentation, background removal, registration and volume quantification. Specific examples of nuclear medicine examinations that can be analyzed using MATLAB algorithms are also mentioned.
Wavelets in Medical Image Processing On Hip Arthroplasty and De-Noising, Segm...IOSR Journals
This document discusses the use of wavelet transforms for medical image processing, specifically for hip arthroplasty. It provides background on wavelet transforms and how they can be used for tasks like de-noising and segmentation. The document then describes the key parameters measured in hip arthroplasty, including parameters from anterior-posterior and anterior-lateral x-rays both before and after prosthesis insertion. It also discusses using the DICOM standard to organize medical image data and extracting information from DICOM files.
Report medical image processing image slice interpolation and noise removal i...Shashank
This document is a project report submitted by Shashank Singh to the Indian Institute of Information Technology. The project involved developing modules for image slice interpolation and noise removal in medical images. Shashank describes developing algorithms for interpolating between image slices and removing noise while preserving true image data. He provides details on implementing the algorithms in Matlab and creating a GUI for noise removal. The document also covers common medical imaging modalities and techniques like CT, MRI, and image processing filters.
The document discusses several algorithms used for medical image compression. It describes the JPEG algorithm which uses discrete cosine transformation and is widely used. It also covers region of interest compression which focuses on important areas. Finally, it examines embedded zerotree wavelet compression and proposes a new unit embedded zerotree wavelet algorithm to improve on existing methods by reducing the number of nodes checked in the wavelet tree structure.
Medical Image Processing Strategies for multi-core CPUsDaniel Blezek
This document discusses various strategies for parallelizing medical image processing tasks across multi-core CPUs. It begins with a poll asking about readers' computer hardware and parallel programming experience. It then covers degrees of parallelism from serial to large-scale parallelism. The document presents pragmatic approaches using C/C++ with "bolted on" parallel concepts. It provides a brief introduction to SIMD and focuses on SMP concepts using threads, concurrency, and parallel programming models like OpenMP, TBB, and ITK. It discusses example problems like thresholding images and common errors. It concludes with next steps in parallel computing.
This is a starter-presentation for folks trying to get their feet wet with medical image processing in a Matlab environment. It discusses certain simple image processing algorithms employed in the context of MR imaging with examples.
This document summarizes current medical image processing research being conducted at various universities. It describes projects involving ECG compression, MRI using blood oxygen level detection, spinal image fusion to improve diagnosis, segmenting anatomical structures from MRI, and using elasticity imaging to detect kidney transplant rejection. It also lists programs for processing MRI data and retinex image processing, as well as websites with medical image test data and news about diagnostic imaging.
Biomedical Image Processing
Topics covered: Biomedical imaging, Need of image processing in medicine, Principles of image processing, Components of image processing, Application of image processing in different medical imaging systems
This document provides an introduction to medical image processing. It discusses various medical imaging modalities like X-ray, CT, MRI, ultrasound, PET, and angiography. It then describes the basic steps in a medical image processing system: acquisition, preprocessing, segmentation, detection, analysis, and diagnosis. Preprocessing techniques like filtering and denoising are discussed. The document concludes by mentioning some applications of medical image processing like compression, retrieval, and tumor detection.
Medical Image Processing on NVIDIA TK1/TX1NVIDIA Taiwan
This document discusses using NVIDIA TK1/TX1 platforms for medical image processing. It summarizes segmenting brain MRI images using fuzzy c-means clustering on these platforms. Performance is evaluated for single and multiple GPU implementations using different precision formats and memory copy modes. Texture-based image processing for mammographic images is also discussed.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
This chapter introduces concepts related to artificial intelligence, machine learning, and deep learning. It specifically discusses convolutional neural networks and their application to building computer-aided diagnosis models for chest x-rays. Key factors for designing chest x-ray CAD models using CNNs are discussed, including network architecture, training types like transfer learning, and data augmentation. The chapter motivates the use of deep learning techniques for medical image analysis and diagnosis to help automate feature learning and improve over traditional CAD systems.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document describes a novel approach to automated classification of brain tumors using probabilistic neural networks (PNN). It discusses how principal component analysis (PCA) can be used to reduce the dimensionality of magnetic resonance (MR) brain images, and then a PNN can classify the tumors. The proposed method involves using PCA for feature extraction and a PNN for classification. This is intended to provide faster and more accurate classification of brain tumors in MR images than conventional human-based methods.
PADDY CROP DISEASE DETECTION USING SVM AND CNN ALGORITHMIRJET Journal
- The document discusses a study on detecting diseases in paddy/rice crops using deep learning algorithms like convolutional neural networks (CNN) and support vector machines (SVM).
- A dataset of rice leaf images was created and a CNN model using transfer learning with MobileNet was developed and trained on the dataset to classify rice diseases.
- The proposed method aims to automatically classify rice disease images to help farmers more accurately identify diseases, as manual identification can be difficult and inaccurate. This could help improve treatment and support farmers.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
A modified residual network for detection and classification of Alzheimer’s ...IJECEIAES
Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer residual neural network (ResNet) is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
This document discusses neural networks and their applications in signal processing. It begins with an introduction to neural networks and their biological inspiration. Applications of neural networks in signal processing are then outlined, including EEG, EMG, medical diagnosis, and more. The document reviews two papers on deep learning applications in biomedical fields and identifies challenges around model building and interpretability. It sets research objectives to optimize mathematical models and improve result interpretation. In conclusion, deep learning has advanced pattern recognition but challenges remain around data availability and model methodology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Prediction of Cognitive Imperiment using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) model to predict cognitive impairment based on MRI data. It describes collecting MRI reports from various sources to create training and test datasets divided into categories for Alzheimer's dementia, healthy controls, and mild cognitive impairment. The CNN model is trained on this data to differentiate between stages of illness. Results showed the CNN approach achieved accuracy of 81.96% for sensitivity, 71.35% for specificity, and 89.72% for precision, outperforming other state-of-the-art methods by around 5%. The proposed system uses CNN to automatically learn features from raw MRI images without need for manual feature extraction, allowing for a more objective and less biased prediction of cognitive impairment.
An Introduction To Artificial Intelligence And Its Applications In Biomedical...Jill Brown
1) The document discusses the use of artificial intelligence techniques in biomedical engineering and medicine. It focuses on using AI to analyze medical signals and images to assist clinicians.
2) Key applications discussed include using neural networks and expert knowledge as intelligent agents to aid diagnosis, as well as using AI systems to automatically realign MRI images and identify corresponding slices from different scans.
3) The document also outlines the major components of AI, including problem solving, knowledge representation, and perception, and how AI can be applied through intelligent agents, a task manager, and a communication system to integrate different types of medical data and decision-making approaches.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
Pneumonia Detection Using Convolutional Neural Network WritersIRJET Journal
This document describes a study that developed a convolutional neural network (CNN) model to detect and classify pneumonia from chest x-ray images without any training. The model was able to extract relevant features from chest x-ray images and determine if a patient has pneumonia. The CNN model achieved high accuracy for pneumonia classification compared to other advanced methods that rely on transfer learning or manual feature engineering. The study used a dataset of 5,500 chest x-ray images and performed image processing techniques like background removal and cropping before extracting features with the CNN and classifying images.
Convolutional Neural Network Based Method for Accurate Brain Tumor Detection ...IRJET Journal
This document proposes a convolutional neural network (CNN) based method for accurate brain tumor detection in MRI images to improve robustness. The method aims to enhance detection accuracy and identify tumor boundaries while differentiating tumor regions from healthy tissue. Experimental results using a large annotated MRI image dataset demonstrate the proposed method achieves superior performance compared to existing approaches. The achieved accuracy, efficiency and specificity validate the effectiveness of the CNN-based method for accurate brain tumor detection, with potential to improve clinical decision-making and patient outcomes.
This document outlines a project on brain tumor detection and diagnosis using convolutional neural networks. It discusses the objective of outlining current automatic segmentation techniques using CNNs. It then provides an introduction on the importance of accurate brain tumor segmentation for diagnosis and treatment. The remaining sections cover literature reviews on CNN segmentation methods, the overall architecture and working principles, applications and the future scope of this area of research.
Similar to Medical Image Processing using a SIMD Array Processor and Neural Networks (20)
This document discusses the impact of data mining on business intelligence. It begins by defining business intelligence as using new technologies to quickly respond to changes in the business environment. Data mining is an important part of the business intelligence lifecycle, which includes determining requirements, collecting and analyzing data, generating reports, and measuring performance. Data mining allows businesses to access real-time, accurate data from multiple sources to improve decision making. Using business intelligence and data mining techniques can help businesses become more efficient and make better decisions to increase profits and customer satisfaction. The expected results of applying business intelligence include improved decision making through accurate, timely information to support organizational goals and strategic plans.
This document presents a novel technique for solving the transcendental equations of selective harmonics elimination pulse width modulation (SHEPWM) inverters based on the secant method. The proposed algorithm uses the secant method to simplify the numerical solution of the nonlinear equations and solve them faster compared to other methods. Simulation results validate that the proposed method accurately estimates the switching angles to eliminate specific harmonics from the output voltage waveform and achieves near sinusoidal output current for various modulation indices and numbers of harmonics eliminated.
This document summarizes a research paper that designed and implemented a dual tone multi-frequency (DTMF) based GSM-controlled car security system. The system uses a DTMF decoder and GSM module to allow a car to be remotely controlled and secured from a mobile phone. It works by sending DTMF tones from the phone through calls to the GSM module in the car. The decoder interprets the tones and a microcontroller executes commands to disable the ignition or control other devices. The system was created to improve car security and accessibility through remote monitoring and control with DTMF and GSM technology.
This document presents an algorithm for imperceptibly embedding a DNA-encoded watermark into a color image for authentication purposes. It applies a multi-resolution discrete wavelet transform to decompose the image. The watermark, encoded into DNA nucleotides, is then embedded into the third-level wavelet coefficients through a quantization process. Specifically, the watermark nucleotides are complemented and used to quantize coefficients in the middle frequency band, modifying the coefficients. The watermarked image is reconstructed through inverse wavelet transform. Extraction reverses these steps to recover the watermark without the original image. The algorithm aims to balance imperceptibility and robustness through this wavelet-based, blind watermarking scheme.
1) The document analyzes the dynamic saturation point of a deep-water channel in Shanghai port based on actual traffic data and a ship domain model.
2) A dynamic channel transit capacity model is established that considers factors like channel width, ship density, speed, and reductions due to traffic conditions.
3) Based on AIS data from the channel, the average traffic flow is calculated to be 15.7 ships per hour, resulting in a dynamic saturation of 32.5%, or 43.3% accounting for uneven day/night traffic volumes.
The document summarizes research on the use of earth air tunnels and wind towers as passive solar techniques. Key findings include:
- Earth air tunnels circulate air through underground pipes to take advantage of the stable temperature 4 meters below ground for cooling in summer and heating in winter. Testing showed the technique can reduce ambient temperatures by up to 14 degrees Celsius.
- Wind towers circulate air through tall shafts to cool air entering buildings at night and provide downward airflow of cooled air during the day.
- Experimental testing of an earth air tunnel system over multiple months found maximum temperature reductions of 33% in spring and minimum reductions of 15% in summer.
The document compares the mechanical and physical properties of low density polyethylene (LDPE) thin films and sheets reinforced with graphene nanoparticles. LDPE/graphene thin films were produced via solution casting, while sheets were made by compression molding. Testing showed that the thin films had enhanced tensile strength, lower melt flow index, and higher thermal stability compared to sheets. The tensile strength of thin films increased by up to 160% with 1% graphene, while sheets increased by 70%. Melt flow index decreased more for thin films, indicating higher viscosity. Thin films also showed greater improvement in glass transition temperature. These results demonstrate that processing technique affects the properties of LDPE/graphene nanocomposites.
The document describes improvements made to a friction testing machine. A stepper motor and PLC control system were added to automatically vary the load on friction pairs, replacing the manual method. Tests using the improved machine found that the friction coefficient decreases as the load increases, and that abrasive and adhesive wear increased with higher loads. The improved machine allows more accurate and convenient testing of friction pairs under varying load conditions.
This document summarizes a research article that investigates the steady, two-dimensional Falkner-Skan boundary layer flow over a stationary wedge with momentum and thermal slip boundary conditions. The flow considers a temperature-dependent thermal conductivity in the presence of a porous medium and viscous dissipation. Governing partial differential equations are non-dimensionalized and transformed into ordinary differential equations using similarity transformations. The equations are highly nonlinear and cannot be solved analytically, so a numerical solver is used. Numerical results are presented for the skin friction coefficient, local Nusselt number, velocity and temperature profiles for varying parameters like the Falkner-Skan parameter and Eckert number.
An improvised white board compass was designed and developed to enhance the teaching of geometrical construction concepts in basic technology courses. The compass allows teachers to visually demonstrate geometric concepts and constructions on a white board in an engaging, hands-on manner. It supports constructivist learning principles by enabling students to observe and emulate the teacher. The design process utilized design and development research methodology to test educational theories and validate the practical application of the compass. The improvised compass was found to effectively engage students and improve their performance in learning geometric constructions.
The document describes the design of an energy meter that calculates energy using a one second logic for improved accuracy. The meter samples voltage and current values using an ADC synchronized to the line frequency via PLL. It calculates active and reactive power by averaging the sampled values over each second. The accumulated active power for each second is multiplied by one second to calculate energy, which is accumulated and converted to kWh. Test results showed the meter achieved an error of 0.3%, within the acceptable limit for class 1 meters. Considering energy over longer durations like one second helps reduce percentage error in the calculation.
This document presents a two-stage method for solving fuzzy transportation problems where the costs, supplies, and demands are represented by symmetric trapezoidal fuzzy numbers. In the first stage, the problem is solved to satisfy minimum demand requirements. Remaining supplies are then distributed in the second stage to further minimize costs. A numerical example demonstrates using robust ranking techniques to convert the fuzzy problem into a crisp one, which is then solved using a zero suffix method. The total optimal costs from both stages provide the solution to the original fuzzy transportation problem.
1) The document proposes using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for a Distributed Power Flow Controller (DPFC) to improve voltage regulation and power quality in a transmission system.
2) A DPFC is placed at a load bus in an IEEE 4 bus system and its performance is compared using a PI controller and ANFIS controller.
3) Simulation results show the ANFIS controller provides faster convergence and better voltage profile maintenance during voltage sags and swells compared to the PI controller.
The document describes an improved particle swarm optimization algorithm to solve vehicle routing problems. It introduces concepts of leptons and hadrons to particles in the algorithm. Leptons interact weakly based on individual and neighborhood best positions, while hadrons (local best particles) undergo strong interactions by colliding with the global best particle. When stagnation occurs, particle decay is used to increase diversity. Simulations show the improved algorithm avoids premature convergence and finds better solutions compared to the basic particle swarm optimization.
This document presents a method for analyzing photoplethysmographic (PPG) signals using correlative analysis. The method involves calculating the autocorrelation function of the PPG signal, extracting the envelope of the autocorrelation function using a low pass filter, and approximating the envelope by determining attenuation coefficients. Ten PPG signals were collected from volunteers and analyzed using this method. The attenuation coefficients were found to have similar values around 0.46, providing a potentially useful parameter for medical diagnosis.
This document describes the simulation and design of a process to recover monoethylene glycol (MEG) from effluent waste streams of a petrochemical company in Iran. Aspen Plus simulation software was used to model the process, which involves separating water, salts, and various glycols (MEG, DEG, TEG, TTEG) using a series of distillation columns. Sensitivity analyses were performed to optimize column parameters such as pressure, reflux ratio, and boilup ratio. The results showed that MEG, DEG, TEG, and TTEG could be recovered at rates of 5.01, 2.039, 0.062, and 0.089 kg/hr, respectively.
This document presents a numerical analysis of fluid flow and heat transfer characteristics of ventilated disc brake rotors using computational fluid dynamics (CFD). Two types of rotor configurations are considered: circular pillared (CP) and diamond pillared radial vane (DP). A 20° sector of each rotor is modeled and meshed. Governing equations for mass, momentum, and energy are solved using ANSYS CFX. Boundary conditions include 900K and 1500K isothermal rotor walls for different speeds. Results show the DP rotor has 70% higher mass flow and 24% higher heat dissipation than the CP rotor. Velocity and pressure distributions are more uniform for the DP rotor at higher speeds, ensuring more uniform cooling. The
This document describes the design and testing of an automated cocoa drying house prototype in Trinidad and Tobago. The prototype included automated features like a retractable roof, automatic heaters, and remote control. It aims to address issues with the traditional manual sun drying process, which is time-consuming and relies on human monitoring of changing weather conditions. Initial testing with farmers showed interest in the automated system as a potential solution.
This document presents the design of a telemedical system for remote monitoring of cardiac insufficiency. The system includes an electrocardiography (ECG) device that collects and digitizes ECG signals. The ECG signals undergo digital signal processing including autocorrelation analysis. Graphical interfaces allow patients and doctors to view ECG data and attenuation coefficients derived from autocorrelation analysis. Data is transmitted between parties using TCP/IP protocol. The system aims to facilitate remote monitoring of cardiac patients to reduce hospitalizations through early detection of health changes.
The document summarizes a polygon oscillating piston engine invention. The engine uses multiple pistons arranged around the sides of a polygon within cylinders. As the pistons oscillate, they compress and combust air-fuel mixtures to produce power. This design achieves a very high power-to-weight ratio of up to 2 hp per pound. Engineering analysis and design of a prototype 6-sided engine is presented, showing it can produce 168 hp from a 353 cubic feet per minute air flow at 12,960 rpm. The invention overcomes issues with prior oscillating piston designs by keeping the pistons moving in straight lines within cylinders using conventional piston rings.
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"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
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💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
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How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
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"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
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What is an RPA CoE? Session 1 – CoE VisionDianaGray10
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Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
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Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
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ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Medical Image Processing using a SIMD Array Processor and Neural Networks
1. International Journal of Engineering Inventions
e-ISSN: 2278-7461, p-ISSN: 2319-6491
Volume 2, Issue 12 (August 2013) PP: 56-64
www.ijeijournal.com Page | 56
Medical Image Processing using a SIMD Array Processor and
Neural Networks
Himadri Nath Moulick, Moumita Ghosh
ABSTRACT: An SIMD parallel implementation of Kass et al's "snake" algorithm is reported, and demonstrated in
use for interpreting medical images. The SIMD machine acts as a "compute server" to a generalpurpose AI
environment, which provides and interactive user interface, or controls the server autonomously. This paper reviews
the application of artificial neural networks in medical image preprocessing, in medical image object detection and
recognition. Main advantages and drawbacks of artificial neural networks were discussed.By this survey, the paper
try to answer what the major strengths and weakness of applying neural networks for medical image processing
would be.
Keywords: Neural Network; Medical Image preprocessing; Object Detection;Computer Aided Diagnosis.
I. Introduction
This paper reports work carried out within the BIOLAB consortium of the CEC Advanced Informatics in
Medicine(AIM) programme. The overall purpose of the work is to assess the potential for commercially available
parallelcomputer systems to enhance practical applications of medical image analysis. Here, we report methods
developedfor using the Active Memory Technology (AMT) DAP as a "compute server" to a network of
workstations. We report three technical developments. • A parallel implementation of the "snake" algorithm of Kass,
Witkin&Terzopoulos, extended to deal with closed contours ("bubbles"). • A software interface between a
developmentenvironment on the remote SUN workstations and the DAP, providing either autonomous or interactive
control of the "bubbles". • A demonstration of the use of the DAP as a more conventional "compute server" to the
workstations, giving access to a set of conventional low-level algorithms, forming part of the AMT
imageprocessinglibrary.The configuration is intended to assess the opportunities for using specialised parallel
computers within a network of biomedical workstations. The DAP acts as a remote-access engine giving
considerably improved performance over a single workstation in the network. The workstation displays the images
to the user and provides an interactive interface, allowing high-level control of the system. The DAP facilities have
been integrated within the Interactive Vision Environment (IVE, initially developed under Alvey grant MMI-007),
which runs within the AI development environment POPLOG. The numerical computing power of the DAP is
thereby coupled to thegeneral-purpose reasoning power only available in serial machines. The benefits and costs of
the SIMD machines such as the DAP in this context are examined and reported. In the past years, artificial neural
networks (ANNs) have seen an an increasingly interests in medical image processing[1]-[2]. According to our
searching results with Google Scholar, more than 33000 items were found on the topic of medical image processing
with ANNs during the past 16 years. The intention of this article is to cover those approaches introduced and to
make a map for ANN techniques used for medical image processing. Instead of trying to cover all the issues and
research aspects of ANNs in medical image processing, we focus our discussion on three major topics: medical
image pre-processing, medical image segmentation, and medical image object detection and recognition. We do not
contemplate to go into details of particular algorithms or describe results of comparative experiments, rather we
want to summarize main approaches and point out interesting parts of the neural networks for medical image
processing, further more by this survey, we try toanswer what the major strengths and weaknesses of applying
ANNs for medical image processing would be.
II. APPLICATIONS OF NEURAL NETWORKS IN MEDICAL IMAGE PROCESSING
Pre-processing
Image pre-processing with neural networks generally falls into one of the following two categories: image
reconstruction and image restoration. The Hopfield neural network is one of the most used neural works for image
reconstruction [3]-[7]. Of our reviewed literatures related to this area, Hopfield neural network based methods pose
55 percent. The major advantage of using Hopfield neural network for medical image reconstruction is that the
problem of medical image reconstruction can be taken as an optimization problem, which is easily solved by letting
the network converge to a stable state while minimizing the energy function. Reconstruction of images in electrical
2. Medical Image Processing using a SIMD Array Processor and Neural Networks
www.ijeijournal.com Page | 57
impedance tomography requires the solution of a nonlinear inverse on noisy data. This problem is typically ill-
conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. The feed
forward neural network [8]-[9] and the self-organizing Kohonen neural network [10]-[11] , which pose 2 of 9papers
among our reviewed literatures, respectively, seem to have more advantage for such medical image reconstruction
compared with other techniques, they can calculate a linear approximation of the inverse problem directly from
finite element simulations of the forward problem. The majority of applications of neural networks in medical image
pre-processing are found in medical image restoration, 13 of 24 papers among our reviewed literatures focused their
interests here [12]-[21]. Among which, one paper for Hopfield neural network, seven papers for the feed forward
neural network, and two papers for fuzzy neural network and for cellular neural network, respectively. In the most
basic medical image restoration approach, noise is removed from an image by filtering. Suzuki et al. developed
neural network based filters (NFs) for this problem [12]-[14]. Suzuki et al. Also proposed a new neural edge
enhancer (NEE) based on a modified multilayer neural network, for enhancing the desired edges clearly from noisy
images [15]. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching
edges, the NEE acquires the function of a desired edge enhancer. Compared with conventional edge enhancers, the
NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the
conventional edge enhancers in similarity to the desired edges.
B. Image segmentation
The feed forward neural network is the most used neural network for medical image segmentation.
Amongour reviewed papers, 6 of 17 papers employed the feed forward network for medical image segmentation
[22]-[27]. Compared with the traditional Maximum Likelihood Classifier (MLC) based image segmentationmethod,
it has been observed that the feed forward neural networksbased segmented images appear less noisy, and the feed
forward neural networks classifier is also less sensitive to the selection of the training sets than the MLC. However,
most feed forward neural network based methods have a very slow convergence rate and require a priori learning
parameters. These drawbacks limited the application of feed forward neural networks in medical image
segmentation. Hopfield neural networks were introduced as a tool for finding satisfactory solutions to complex
optimization problems. This makes them an interesting alternative to traditional optimization algorithms for medical
image reconstruction which can be formulated as optimization problem. Among our reviewed literatures, 4 of 17
paper used Hopfield neural network to segment some organs from a medical image [28]-[31].
C. Object detection and recognition
For using neural networks for medical image detection and recognition, the back propagation neural
networkposes most places, 11 of 23 papers among our reviewed literatures employed it [37]-[47]. Compared
withconventional image recognition methods, no matter used for the interpretation of mammograms [37], or used
forcold lesion detection in SPECT image [38], or used for diagnosing classes of liver diseases based
onultrasonographic [39], or used for separation of melanoma from three benign categories of tumors [41],
ordistinguish interstitial lung diseases [42], or used for reduction of false positives in computerized detection oflung
nodules in LDCT [43]-[46]and chest radiography [47], all these feed forward neural network basedmethods show
their preference in recognition accuracy and computing time compared with conventionalmethods. Other neural
networks, i.e. Hopfield neural network [48], ART neural network [49], radial basis function neural network [50],
Probabilistic Neural Network [51], convolution neural network [53]-[56], and fuzzy neural network [52] [57], have
also found their position in medical image detection and recognition, which poses 1 of 23, 1 of 23, 1 of 23, 1 of 23, 2
of 23 and 2 of 23 papers, espectively.Different from what mentioned above, in [58] and [59], artificial neural
network ensembles are employed for cancer detection. The ensemble is built on two-level ensemble architecture.
The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual
network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks
are combined by some a method. The second-level ensemble is used to deal with the cells that are judged as cancer
cells by the first-level ensemble, where each individual network has several outputs respectively, each of which
represents a different type of lung cancer cells. The predictions of thoseindividual networks are combined by a
prevailing method, i.e. plurality voting. Experiments show that the neural network ensemble can achieve not only a
high rate of overall identification but also a low rate of false negativeidentification, i.e. a low rate of judging cancer
cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.
3. Medical Image Processing using a SIMD Array Processor and Neural Networks
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III. THE AMT-DAP 510: OVERVIEW
The DAP is a commercially available array processor, whose basic design has not changed since the 1970s.
It represents a cost-effective way of increasing raw computing power for some applications. The DAP 510 processor
consists of an array of 32 by 32 processor elements (PEs), connected in a fixed square grid (AMT, 1988). Each PE is
a 1-bit computer, running at a clock rate of 10MHz. This gives each processor about the power of a PC or a SUN 3,
but all arithmetic is carried out bit-serial, so the realisable performance greatly depends on the algorithms employed.
All PEs carries out instructions under the control of a single program. Each PE has 4Kbytes of local memory which
is also used to store the images. There is no general shared memory.The DAP has a hi-resolution, colour graphics
monitor, served by a frame-buffer that can be read or written in parallel by the PEs. The DAP is interfaced to a host
SUN 3/50, by means of a SCCSI port, which carries all communications between the DAP and the host (and hence
the SUN network). The DAP is normally programmed in an extended form of Fortran which allows automatic use of
vector and matrix parallelism. A library of standard control software on the host can be called from either Fortran or
C. This includes software for passing data to and from the DAP, loading, starting and interrupting the DAP, and for
passing SUN mouse information to the DAP.
A. Implementation
The DAP510 comprises an array of 32x32 processors. We therefore define each bubble as one or more
sequences of 32 control points, each point being assigned to one processor. The full array is then able to compute 32
sequences inparallel, which can be assigned to arbitrary combinations of bubbles each having multiples of 32 points
(e.g. 32 x 32-point bubbles, 4 x 256-point bubbles, 1 x 1024-point bubble etc). The positions of each control point is
advanced stepwise in time, and each timestep comprises three phases. During the first or FORCE phase, the local
gradient of the image is found at each control point. This requires random access to the 512x512 byte image, stored
amongst the PEs. This is achieved by a tightly coded scalar loop in APAL assembler code. In the next or MOVE
phase, the net force at a point is computed from the local image force, internal forces due to the positions of its
neighbours (tension and stiffness), and the internal pressure term. The equations of motion are:
In these equations, s is a parametric variable advancing by unity between successive control points. The
first term on the RHS gives the effect of an elastic string with an elastic constant T, which acts as a variable tension
in the string proportional to the separation between bubble points. With this term alone, the exact solution between
two fixed points is a straight line with equidistant bubble points, as one would expect for an elastic string. The
second term on the RHS provides the bubble with stiffness determined by the value of s, and a tendency to resist
sharp changes of curvature. The third term is a constant internal pressure force which acts along the line joining the
centre of gravity of the bubble points to the surface bubble point in question. In order to prevent the pressure force
from translating the bubble in the absence of an image, the average value of the pressure force over all the bubble
points is made zero. The last term is the gradient of the image intensity I, or the image force term previous
calculated in the FORCE phase. The solution of the equations of motion is obtained by finite differencing, and
stability for any sized timestep is ensured by using an implicit differencing scheme (see[1]for details). This leads to
a five-diagonal set of equations for the new positions of each bubble, that is to say the need to solve 32 five-diagonal
sets of equations, first for x then for y. The parallel solution is obtained using the method of cyclic reduction,
actually an extension to a five-diagonal matrix of the PARACR algorithm described in reference [2]. Finally, the last
or GRAPHICS phase of the timestep uses the directly connected DAP display monitor to plot the new positions of
all snake points on the image. Having found all the new bubble positions in parallel, the timestep is repeated
continuously, and the bubbles relax towards positions tending to lie along the bubble-shaped objects in the image.
However, even when the points are stationary, the DAP is continuously computing them; that is to say the bubbles
are 'alive' at all times, and will respond instantly if any points are interactively moved.
B. Control of Shape and Size
In the absence of an image, the steady state solution to the above bubble equations with periodic (or cyclic)
boundary conditions in the s-variable, and constant values for tension and stiffness, is a circle. If the stiffness is zero
then the radius of the circle is proportional to the ratio of pressure to tension, just as it would be for a soap bubble.
4. Medical Image Processing using a SIMD Array Processor and Neural Networks
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The introduction of stiffness slightly decreases the radius of the circle. The pressure can therefore be conveniently
used to vary the size of the bubble in a natural way. If the tension is varied around the bubble surface (i.e. as a
function of the variable s), then the shape of the bubble can be varied. Different harmonic functions may be applied
to the elastic tension and summed. In principle this provides a simple means for defining a wide range of closed,
convex shapes. Limited experiments have been carried out using the second and third harmonics, Massing the
bubble towards given elliptical or ovoidal shapes.
Fig 1. A single 32 node bubble interacting with an isolated cell (a) original image, (b) modulus of thesmoothed
gradient, (c) the bubble overlaid on (a).
C. Examples
Fig1 demonstrates a simple interaction between a 32- point bubble and an image of an isolated cell. The
image,Figl(a), was first smoothed by a 2-D gaussian distribution (a = 1 pixel), and an image corresponding to the
modulus of the discrete gradient vector was computed, Fig l(b). A small bubble was placed inside the cell in Figl(b).
Under the influence of thepressure term, the bubble grew, until it was caught and held by the image maxima. When
stable, the bubble's position outlines the cell boundary fairly accurately - Fig l(c). The time to stabilise depends on
the match between the initial shape parameters of the bubble, and the image distribution. It is strongly affected by
the time-step used in the algorithm. A large time-step causes the bubble to respond rapidly to the forces on it, but
this can lead to instability and oscillation. In the examples shown here, the convergence time (for each bubble) was
approximately 2-4 seconds. Better convergence schemes, based on simulated annealing methods can improve
performance (see below).The power of the parallel implementation of bubbles is more evident in images consisting
of multiple cells. Fig 2 shows 32 bubbles interacting with an edge-differentiated cell image. The centres of suspected
cells were first identified by finding peaks in a smoothed Laplacian image (where the scale of the smoothing is
adjusted to suit the expected cell size.). Bubbles were then placed in the image at these points. In most cases the
bubbles have adjusted their shape to that of the actual cells and have become attached to them. The settling time for
all 32 bubbles is again about 2 seconds.
D. Performance
The performance of the AMT DAP relative to a typical workstation was measured by having the host
computerperform the same calculation at the same time as the DAP.Timings were obtained individually for the
different phases of the timestep for both the DAP and the SUN, and the overall speedup ratio of the calculation was
calculated. Timings given below refer to the SUN 3/50 host machine.
Fig 2 .1J bubble parallel placed automatically in
5. Medical Image Processing using a SIMD Array Processor and Neural Networks
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These timings show that on the DAP only 10% of the time is spent solving the motion equations - the bulk
of the time is consumed in the FORCE calculations and the GRAPHICS output The FORCE phase is dominated by
the essentially serial random image access routines, using carefully coded assembler routines in APAL which picks
up one byte from the image at a time.
Fig 3.showing two bubbles, interacting with the scalp outline and the cortical surface
Comparisons using a SUN 4/20 indicate that (as expected), the SUN 4 performs about 10 times as fast as
the SUN3. This gives a speed-up advantage for the DAP of merely 4. However, when used in the networked
configuration (see below) the graphics phases and the control parameter processing of the DAP program can be
dropped. This restores the advantage to about a factor of 10. It should be noted that the DAP-510 has fairly low
processing power: a larger DAP can be configured with 64*64 PEs, and a faster machine, with 8-bit arithmetic is
scheduled. In this work the DAP is used to represent SIMD compute servers; the bubble algorithm could easily be
adapted to other, faster SIMD machines. More modern machines have mechanisms for shared memory, with indirect
addressing in the PEs, which would greatly speed up the calculations in the FORCE phase of the program.
IV. CONTROL MECHANISMS
Bubbles can only act to refine the localisation of structure in the image, since they must initially be placed
close enough to an appropriate attractor. Unguided, they merely fall into arbitrary local minima. One potential
practical role for deformable models in medical image processing applications is to provide an "intelligent" tool to
assist the clinician inmaking interactive measurements of image features, and our initial DAP deformable model
algorithm was developed for such direct use.
A. Work station control
The DAP graphics screen only allows single-user access. At present, such under-use of an array processor
cannot be
Fig 4. Close-up view of part of Fig3., showing two bubbles, interacting with the scalp outline and
the cortical surface justified on cost grounds. Medical image processing applications typically occur intermittently.
It is thereforeattractive to seek to network the DAP as a "compute server" for many workstations. This has the
additional advantage that the GRAPHICS part of the algorithm can be delegated to the workstation, thus increasing
the performance by about a factor of 2 (see table). The DAP-SUN SCCSI link gives the host computer full control of
the DAP. The DAP program repeatedly checks the host SUN for messages, requesting a read or write of either the
6. Medical Image Processing using a SIMD Array Processor and Neural Networks
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system parameters or the position of the control points. This allows a program written on the host machine to control
the compute engine. To provide for user interaction with the program on the SUN workstations, we have developed
a simple link between the DAP and POPLOG running on SUN 3 or 4 (or other) machines within the network.
Within POPLOG, the DAP engine has been integrated into a vision systems development tool developed and used
extensively in house (the Interactive Vision Environment - IVE). Fig 3 shows a screen dump from a typical session.
This facility allows the DAP programs to be used on a singleuserbasis across the network. Multiple users
aremanaged by the existing multi-tasking operating system of the DAP. In a dedicated system, a remote demon is
envisaged to improve the efficiency of time sharing between multiple workstations.
V. DISCUSSION
We have described a SIMD implementation of the algorithms reported by Kass, Witkin and Terzopoulos.
The use of the parallel computer makes new methods for interactive image processing feasible. Such processing is
likely to be of particular importance in the domain of medical image processing, where the clinician often needs to
control the analysis of an image by hand, yet would greatly welcome increased assistance from the computer in
outlining important structures. Much time is currently spent in radiological laboratories to identify structures in
images manually, to identify regions which require further processing. The example shown in Fig 1 is of a cell from
a cervical smear: the degree of irregularity in the boundary of the cell is a primary indicator of a pre-cancerous
condition. The cells in Fig 2 have been stained by means of mono-clonal antibodies which attach to the active sites
involved in rheumatoid arthritis; the type of staining, and the size and shape of the stained cells are of importance for
contemporary research into the causes and treatment of rheumatism. Active deformable models can form a variety of
structures. The smooth, closed "bubbles" described here have particular use in cytological applications, but other
structures are currently being investigated. Fig 4 shows the outline scalp extracted from MR slice images. Each slice
was pre-filtered to create an image of the modulus of the gradient. A bubblewas placed near the edge of the image,
outside the head region, with low pressure. It rapidly shrank and was trapped onto the outer gradient peak. The
elastic term was then reduced, so that the position of the bubble was dominated by the image data. Fig 3 and 4 were
captured during this operation. The cortical boundary has also been approximated in these images by a second
bubble, half the size of the first bubble, placed inside the scalp and then expanded by increasing the pressure. This
reliably avoids many of the artifacts associated with theuse of edge maps, as reported in Attwood et al (1990). In
principle it should be possible to process multiple slices simultaneously, though software to do this is still under
development. This would allow 32 slices such as are shown in Fig 4 to be computed in parallel, and outlines similar
to these to be computed in a few seconds. The further use of DAP graphics routines for viewing and shading the
results are also intended. Two other extensions of this work are of special interest: (i) The use of higher order terms
in the definition of the shape of the bubble. Bubbles are biassed by the harmonics in the elasticity, and by the choice
of starting conditions. It is therefore possible to model more complex structures, such as the ventricles of the heart in
cine-angiography, or organs of the brain in cranial radiography. These models can be stored in a database of
anatomical shapes, called up according to the clinical context, (ii) Methods for forming 2-D rubber sheets, in a
32*32 array. Many medical imaging methods produce series of slices such as in Figure 4, which capture the 3-D
structure of organs being imaged. It is straightforward to extend the ID snakes (moving in a 2D image) to
2Dmembranes (moving in 3D data). This will provide the ability to "shrink-wrap" 3D structures directly, rather than
building them from independently processed slices.
VI. CONCLUSION
From the reviewed literatures, we find that no matter what neural network model employed for medical
imageprocessing, compared with conventional image processing methods, the time for applying a trainedneural
network to solve a medical image processing problem was negligibly small, though the training of a neural network
is a time cost work and also medical image processing tasks often require quite complex computation [12]-[15]. We
think that this may be the major contribution of using neural network for solving medical image processing
tasks.Despite their success story in medical image processing, artificial neural networks have several major
disadvantages compared to other techniques. The first one is that a neural network is hard to express human expert’s
knowledge and experience, and the construction of its topological structure lacks of theoretical methods [60].
Moreover the physical meaning of its joint weight is not clear. All these can make the image processing method by
neural networks unstable. A solution to these problems may be to combines fuzzy technique with neural networks
together by using neural networks to process fuzzy information. It provides neural networks the ability to express
qualitative knowledge, and network topological structure and joint weight have clear physical meaning. Also, it can
make the initialization of network easier, avoid the local optimization of network training, and ensure the stability of
7. Medical Image Processing using a SIMD Array Processor and Neural Networks
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networks [61]. The second problem relates to the amount of input data. For achieving a high and reliable
performance for non-training cases, a large number of training cases are commonly required [62] [63]. If an ANN is
trained with only a small number of cases, the generalization ability(performance for non-training cases) will be
lower (e.g., the ANN may fit only the training cases). Becausemedical images are progressing rapidly as technology
advances, the timely development of CAD schemes isimportant. However, it is very difficult to collect a large
number of abnormal cases for training, particularly for aCAD scheme with a new modality, such as lung cancer
screening with multi-detector-row CT. This significantlydegraded the results obtained. The very high speed
implementation on a parallel machine makes the techniques of deformable models feasible for clinical and research
applications. The speed-up achieved transforms algorithms for deformable models from acomputational curiosity, to
one which would appear to have immediate practical significance. This implementation is based on an SIMD
machine, available commercially for the cost of a typical mini-computer. This order of cost can be justified in a
networked, multi-tasking system.
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