Minimization of radiation dose plays an important role in human wellbeing. Excess of radiation dose leads to cancer. Radiation greatly affects young children less than 10 years of age as their life span is longer. Radiation can be reduced by hardware and/or by software techniques. Hardware methods deal with variation of parameters such as tube voltage, tube current, exposure time, focal distance and filter type. Software techniques include image processing methods. The originally acquired X-ray images may be contaminated with noise due to the fact of instability in the case of sensors, electrical power or X-ray source, that is responsible for the degradation of the image attributes. An enhanced image denoising algorithm has been proposed which decreases Gaussian noise combined with salt and pepper noise that retains most information particulars.
This document describes a new microwave imaging system for breast cancer detection that produces 3D tomographic images much faster than previous systems. The system uses an array of antennas to illuminate the breast and collects data in under 2 minutes. It then uses a discrete dipole approximation algorithm to reconstruct the 3D images in less than 20 minutes, overcoming the enormous time burdens of prior algorithms. The document presents the first clinical 3D microwave tomographic images of the breast from over 400 patient exams. Two clinical examples are shown, one demonstrating potential for breast cancer screening and another focusing on monitoring therapy response.
Evaluation of SVM performance in the detection of lung cancer in marked CT s...nooriasukmaningtyas
This document summarizes a study that evaluated the performance of support vector machines (SVM) in detecting lung cancer using a new computed tomography (CT) scan dataset from Iraq. The dataset contains over 1,100 images from 110 cases classified as normal, benign, or malignant. A computer system was proposed that applied preprocessing techniques like enhancement, segmentation, and feature extraction before using SVM for classification. Different SVM kernels and feature extraction methods were evaluated. The best accuracy achieved on this dataset using this approach was 89.88%.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
IRJET- Brain Tumor Detection using Image Processing and MATLAB ApplicationIRJET Journal
This document presents a proposed method for detecting brain tumors in MRI scans using image processing techniques in MATLAB. It begins with an introduction to brain tumors, MRI, and causes. The proposed method uses anisotropic filtering to reduce noise, SVM classification to segment tumor regions, and morphological operations like dilation and erosion to extract the tumor boundaries. The MATLAB application provides a graphical user interface with tabs for viewing input images, filtered outputs, and detected tumors. Testing on sample images achieved tumor detection times ranging from 152-733 milliseconds depending on image properties. Future work could involve extending the method to 3D images, implementing machine learning for dynamic thresholding, and detecting smaller malignant tumors.
IRJET- Literature Review on Identification of Malignant Region in Human BodyIRJET Journal
This document discusses using thermal infrared imaging to identify malignant regions in the human body. It begins with an overview of cancer and different imaging techniques used for detection like X-ray, MRI, and optical imaging. The advantages of thermal imaging are discussed, including its ability to detect differences in surface temperature that could indicate abnormal cell growth. The document then focuses on thermal imaging in more detail, outlining the process of preprocessing, feature extraction, and analyzing thermal images to identify potential malignant regions based on changes in temperature. It concludes that thermal infrared imaging shows potential for non-invasive cancer detection but requires further clinical studies to develop standardized protocols.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
This document describes a new microwave imaging system for breast cancer detection that produces 3D tomographic images much faster than previous systems. The system uses an array of antennas to illuminate the breast and collects data in under 2 minutes. It then uses a discrete dipole approximation algorithm to reconstruct the 3D images in less than 20 minutes, overcoming the enormous time burdens of prior algorithms. The document presents the first clinical 3D microwave tomographic images of the breast from over 400 patient exams. Two clinical examples are shown, one demonstrating potential for breast cancer screening and another focusing on monitoring therapy response.
Evaluation of SVM performance in the detection of lung cancer in marked CT s...nooriasukmaningtyas
This document summarizes a study that evaluated the performance of support vector machines (SVM) in detecting lung cancer using a new computed tomography (CT) scan dataset from Iraq. The dataset contains over 1,100 images from 110 cases classified as normal, benign, or malignant. A computer system was proposed that applied preprocessing techniques like enhancement, segmentation, and feature extraction before using SVM for classification. Different SVM kernels and feature extraction methods were evaluated. The best accuracy achieved on this dataset using this approach was 89.88%.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
IRJET- Brain Tumor Detection using Image Processing and MATLAB ApplicationIRJET Journal
This document presents a proposed method for detecting brain tumors in MRI scans using image processing techniques in MATLAB. It begins with an introduction to brain tumors, MRI, and causes. The proposed method uses anisotropic filtering to reduce noise, SVM classification to segment tumor regions, and morphological operations like dilation and erosion to extract the tumor boundaries. The MATLAB application provides a graphical user interface with tabs for viewing input images, filtered outputs, and detected tumors. Testing on sample images achieved tumor detection times ranging from 152-733 milliseconds depending on image properties. Future work could involve extending the method to 3D images, implementing machine learning for dynamic thresholding, and detecting smaller malignant tumors.
IRJET- Literature Review on Identification of Malignant Region in Human BodyIRJET Journal
This document discusses using thermal infrared imaging to identify malignant regions in the human body. It begins with an overview of cancer and different imaging techniques used for detection like X-ray, MRI, and optical imaging. The advantages of thermal imaging are discussed, including its ability to detect differences in surface temperature that could indicate abnormal cell growth. The document then focuses on thermal imaging in more detail, outlining the process of preprocessing, feature extraction, and analyzing thermal images to identify potential malignant regions based on changes in temperature. It concludes that thermal infrared imaging shows potential for non-invasive cancer detection but requires further clinical studies to develop standardized protocols.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
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.
Deep Learning for Pneumonia Diagnosis: A Comprehensive Analysis of CNN and Tr...IRJET Journal
This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve patient outcomes.
All medical imaging equipment manufactured today is supposed to conform to the DICOM standards. Viewing of the images thus produced cannot be done by ordinary imaging programs available on a regular PC. A special diagnostic medical imaging program is required, known as a DICOM workstation. For commercial use in medical diagnosis, such diagnostic medical imaging programs need to be FDA approved and need a special license. These measures ensure that any application developed for clinical purposes is capable of accurate depiction of high quality medical images.
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 -Malignancy Detection using Pattern Recognition and ANNSIRJET Journal
This document discusses using pattern recognition and artificial neural networks (ANNs) to detect malignancy, specifically melanoma skin cancer. It describes preprocessing dermoscopy images to remove noise, then implementing an ANN with 12 neurons in each layer to classify images as cancerous or non-cancerous based on 12 selected features. After training the ANN, it is tested on new data for decision making. The method provides efficient classification compared to alternative gradient descent approaches that may result in incorrect predictions. Publicly available skin cancer data is used to train and validate the ANN model.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
A Comprehensive Study on the Phases and Techniques of Breast Cancer Classific...IRJET Journal
This document provides a comprehensive summary of techniques for breast cancer classification. It discusses three main phases: pre-processing, segmentation, and classification. For pre-processing, techniques like noise suppression, background removal, and edge detection are used. Segmentation involves separating the area of concern from breast tissue. Classification classifies tumors as benign or malignant. The document reviews various algorithms for each phase, finding techniques like Canny edge detection and Hough's line transform effective for segmentation. It also discusses challenges like limited radiologist resources and potential for human error in diagnosis.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
IRJET- Review Paper on a Review on Lung Cancer Detection using Digital Image ...IRJET Journal
This document reviews techniques for detecting lung cancer from digital chest images. It discusses how digital image processing techniques like preprocessing, segmentation, feature extraction and neural networks can be used to analyze CT scan images and detect lung cancers. Preprocessing steps include grayscale conversion, normalization, noise reduction and binary conversion. Segmentation methods like thresholding are used to isolate the lungs. Features like size and shape are extracted and analyzed by neural networks to classify lesions and detect cancers. The paper suggests this automated detection system could help address limitations of manual radiologist review like missed cancers.
IRJET - Fusion of CT and MRI for the Detection of Brain Tumor by SWT and Prob...IRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
The document discusses watermarking techniques for medical images to preserve the region of interest (ROI) during transmission. It first provides background on the need for security in sharing medical images over networks. It then summarizes various techniques for segmenting the ROI from medical images, including thresholding, clustering, and edge detection methods applied to MRI and CT scans. The goal of the watermarking is to apply marks only to the region outside the ROI (RONI) to authenticate images without affecting diagnosis.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
This document discusses the benefits of bringing diagnostic imaging services in-house in the emergency department (ED). It notes that as diagnostic capabilities have expanded, imaging has played an increasingly important role in ED evaluation and management. Housing imaging within the ED can improve efficiency by reducing imaging turnaround times. The document provides guidelines for selecting appropriate imaging modalities for an in-house ED based on annual imaging volumes, patient populations, distance from main imaging services, and staffing. Factors like imaging location within the ED and availability of prior studies can also impact turnaround times and unnecessary repeat imaging. Careful planning of in-house ED imaging can enhance patient care, efficiency and reduce costs.
IRJET - Lung Disease Prediction using Image Processing and CNN AlgorithmIRJET Journal
This document summarizes a research paper that proposes a method for predicting lung disease using image processing and convolutional neural networks (CNNs). The method involves preprocessing chest x-ray images through steps like lung field segmentation, feature extraction, and then classifying the images as normal or abnormal using neural networks and support vector machines (SVMs). The researchers tested their approach on two datasets and were able to classify digital chest x-ray images into normal and abnormal categories with high accuracy. The goal of the research is to develop an automated system for early detection of lung cancer using chest x-rays, as early detection is key to better treatment outcomes.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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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.
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This document summarizes a research paper that proposes using convolutional neural networks and transfer learning to accurately diagnose pneumonia from chest x-rays. The paper describes how pneumonia affects the lungs and the importance of early detection. It discusses how CNNs and transfer learning have been successfully used for medical image classification. The proposed model uses pre-trained CNN architectures like MobileNet, Inception, ResNet and EfficientNet applied to a dataset of chest x-rays to distinguish between normal and pneumonia cases. The model achieves highly accurate pneumonia detection, which could help improve patient outcomes.
All medical imaging equipment manufactured today is supposed to conform to the DICOM standards. Viewing of the images thus produced cannot be done by ordinary imaging programs available on a regular PC. A special diagnostic medical imaging program is required, known as a DICOM workstation. For commercial use in medical diagnosis, such diagnostic medical imaging programs need to be FDA approved and need a special license. These measures ensure that any application developed for clinical purposes is capable of accurate depiction of high quality medical images.
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 -Malignancy Detection using Pattern Recognition and ANNSIRJET Journal
This document discusses using pattern recognition and artificial neural networks (ANNs) to detect malignancy, specifically melanoma skin cancer. It describes preprocessing dermoscopy images to remove noise, then implementing an ANN with 12 neurons in each layer to classify images as cancerous or non-cancerous based on 12 selected features. After training the ANN, it is tested on new data for decision making. The method provides efficient classification compared to alternative gradient descent approaches that may result in incorrect predictions. Publicly available skin cancer data is used to train and validate the ANN model.
1. Researchers developed an X-ray disease identifier using a deep learning model to analyze chest X-ray images and diagnose diseases.
2. They used the VGG19 classification model to process X-ray images from the NIH dataset and diagnose diseases, achieving over 60% accuracy for most diseases.
3. The system aims to assist radiologists by providing automated disease diagnoses from X-ray images to reduce their workload and enable diagnoses in remote areas.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
A Comprehensive Study on the Phases and Techniques of Breast Cancer Classific...IRJET Journal
This document provides a comprehensive summary of techniques for breast cancer classification. It discusses three main phases: pre-processing, segmentation, and classification. For pre-processing, techniques like noise suppression, background removal, and edge detection are used. Segmentation involves separating the area of concern from breast tissue. Classification classifies tumors as benign or malignant. The document reviews various algorithms for each phase, finding techniques like Canny edge detection and Hough's line transform effective for segmentation. It also discusses challenges like limited radiologist resources and potential for human error in diagnosis.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
IRJET- Review Paper on a Review on Lung Cancer Detection using Digital Image ...IRJET Journal
This document reviews techniques for detecting lung cancer from digital chest images. It discusses how digital image processing techniques like preprocessing, segmentation, feature extraction and neural networks can be used to analyze CT scan images and detect lung cancers. Preprocessing steps include grayscale conversion, normalization, noise reduction and binary conversion. Segmentation methods like thresholding are used to isolate the lungs. Features like size and shape are extracted and analyzed by neural networks to classify lesions and detect cancers. The paper suggests this automated detection system could help address limitations of manual radiologist review like missed cancers.
IRJET - Fusion of CT and MRI for the Detection of Brain Tumor by SWT and Prob...IRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.
ANALYSIS OF WATERMARKING TECHNIQUES FOR MEDICAL IMAGES PRESERVING ROI cscpconf
The document discusses watermarking techniques for medical images to preserve the region of interest (ROI) during transmission. It first provides background on the need for security in sharing medical images over networks. It then summarizes various techniques for segmenting the ROI from medical images, including thresholding, clustering, and edge detection methods applied to MRI and CT scans. The goal of the watermarking is to apply marks only to the region outside the ROI (RONI) to authenticate images without affecting diagnosis.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
This document discusses the benefits of bringing diagnostic imaging services in-house in the emergency department (ED). It notes that as diagnostic capabilities have expanded, imaging has played an increasingly important role in ED evaluation and management. Housing imaging within the ED can improve efficiency by reducing imaging turnaround times. The document provides guidelines for selecting appropriate imaging modalities for an in-house ED based on annual imaging volumes, patient populations, distance from main imaging services, and staffing. Factors like imaging location within the ED and availability of prior studies can also impact turnaround times and unnecessary repeat imaging. Careful planning of in-house ED imaging can enhance patient care, efficiency and reduce costs.
IRJET - Lung Disease Prediction using Image Processing and CNN AlgorithmIRJET Journal
This document summarizes a research paper that proposes a method for predicting lung disease using image processing and convolutional neural networks (CNNs). The method involves preprocessing chest x-ray images through steps like lung field segmentation, feature extraction, and then classifying the images as normal or abnormal using neural networks and support vector machines (SVMs). The researchers tested their approach on two datasets and were able to classify digital chest x-ray images into normal and abnormal categories with high accuracy. The goal of the research is to develop an automated system for early detection of lung cancer using chest x-rays, as early detection is key to better treatment outcomes.
ARTIFICIAL INTELLIGENCE BASED COVID-19 DETECTION USING COMPUTED TOMOGRAPHY IM...IRJET Journal
This document summarizes an artificial intelligence system developed to detect COVID-19 in computed tomography (CT) images of the lungs. The system uses convolutional neural networks (CNNs) to extract features from segmented lung images and classify images as normal, COVID-19, or other lung diseases. Previous related work that used CNNs and other deep learning techniques on CT and X-ray images for COVID-19 detection is reviewed. The proposed system applies edge detection algorithms before training the CNN to enhance image contrast and improve COVID-19 detection accuracy. It also uses multi-image augmentation to increase the size and variability of the training dataset.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Similar to An approach for radiation dose reduction in computerized tomography (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
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An approach for radiation dose reduction in computerized tomography
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 1169~1179
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp1169-1179 1169
Journal homepage: http://ijece.iaescore.com
An approach for radiation dose reduction in computerized
tomography
Shama Bekal Narayan, Savitha Halkare Mahabaleshwara
Department of Electronics and Communication Engineering, Faculty of Engineering, St Joseph Engineering College, Mangalore, India
Article Info ABSTRACT
Article history:
Received Feb 2, 2022
Revised Sep 23, 2022
Accepted Oct 8, 2022
Minimization of radiation dose plays an important role in human wellbeing.
Excess of radiation dose leads to cancer. Radiation greatly affects young
children less than 10 years of age as their life span is longer. Radiation can
be reduced by hardware and/or by software techniques. Hardware methods
deal with variation of parameters such as tube voltage, tube current,
exposure time, focal distance and filter type. Software techniques include
image processing methods. The originally acquired X-ray images may be
contaminated with noise due to the fact of instability in the case of sensors,
electrical power or X-ray source, that is responsible for the degradation of
the image attributes. An enhanced image denoising algorithm has been
proposed which decreases Gaussian noise combined with salt and pepper
noise that retains most information particulars.
Keywords:
Hardware
Image
Radiation
Software
X-ray This is an open access article under the CC BY-SA license.
Corresponding Author:
Shama Bekal Narayan
Department of Electronics and Communication Engineering, Faculty of Engineering, St Joseph
Engineering College, Affiliated to Visveshwaraya Technological University
Vamanjoor, Mangalore, Karnataka 575028
Email: shamabn@sjec.ac.in
1. INTRODUCTION
Diagnostic examination is a medical specialty procedure which involves medical procedures to
identify and treat diseases. Non-invasive methods like X-ray radiography and computed tomography (CT) are
used to diagnose or treat diseases. The X-rays pass across the human body, creating a computer display.
Direct digital radiography produces an image instantly on a computer screen. Radiologists can access the
image quickly. CT uses X-rays to recognize and note the radiation absorbed by different organs. Radiology
technicians/doctors use these images to analyze bone fractures and other illnesses in the human body [1].
These medical examinations present both benefits and threats. The benefits of these examinations
far exceed the risks. They are fast, painless and non-invasive. Examinations provide detailed information to
identify the issue, treatment plan, and assess many complications in adults and children. In addition, the
accurate images provided by CT scans may waive off the surgery [2]. Dental radiology plays a vital role in
pediatric patients. Recognizing the variety of retraction of chin is critical for distinct investigation in
research. Dentofacial skeletal features differ based on the type of malocclusion [3]. The investigation done on
the juvenile idiopathic arthritis (JIA) recommends children and youngsters are influenced with unilateral or
bilateral balanced to serious temporomandibular joint (TMJ) issues [4]. The cephalometric X-ray supports the
dentist to acquire an entire radiographed picture of the side of the face. Cephalometric regularizing quantum
values for the 8-to-12-year-old South Italian children population was referred to in study. Important
characteristic features among boys and girls in the range of the anterior cranial base and ratio obtained are
recorded [5].
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Reducing the radiation helps in reducing the risk of cancer. Radiation can be reduced by using
hardware and software techniques. Hardware approaches include the analysis of five exposure parameters
and software approaches involve implementation of distinct algorithms. The exposure to ionizing radiation
may lead to a slight raise in an individual's lifetime risk of originating malignancy or cancer [6]. As a person
is exposed to radiation, risk of developing cancer also increases. Risk is greater in children’s as they have
longer life spans. Possibility of raising cancer boosts with periodic subjection to diagnostics imaging
methodologies. Probability is twofold in case of expecting mothers and in offspring. A baby in the womb
may also be more sensitive to radiation than an adult. Scientific research facilitates better evaluation of the
risks. It is observed that as years pass on radiation dose is reduced. Dose can be reduced by both hardware and
software techniques. Hardware techniques include five important exposure confines namely tube voltage (kV),
tube current (mA), exposure time (ms), focus to detector distance (cm) and filter type. Software approach
involves distinct algorithms. Aim is to minimize noise and import clarity to the image by applying rebuilding
techniques and required filters. If the clarity in image is good, doctors can diagnose the issue of patients. If the
image is clear then radiation dose can be reduced. Objective is: i) To reduce radiation dosage in X-ray,
especially for pediatric use and reduce the risk of cancer; ii) To develop an improved image filtering
algorithm to support the reduction of radiation dose; iii) To identify and filter the various noises present in
images; and iv) To enhance the images for scene visibility, enhance the contrast and edges in the X-ray
images. Scope includes long term health benefits to individuals, health benefits to Oncology patients who are
exposed to radiation therapy, in future, state of art algorithms can be implemented by X-ray manufactures
such as Siemens, Philips and General Electric (GE) for health care application.
2. METHOD
Radiation dose reduction [7] follows two major approaches. Hardware and software methods [8].
Figure 1 illustrates different approaches to radiation reduction. Hardware technique includes modification in
the geometry of X-ray machines. As per data sheets of top medical device manufacturing company,
Radiation induced depends on tube voltage in Kilovolts, tube current in milliamperes, exposure time in
milliseconds, prefilter, and focal spot.
Figure 1. Flowchart - techniques of radiation dose reduction
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Radiation builds on a patient body mass index. Radiograph energy is directly proportional to patient
thickness. Reputed medical device manufacturing company has inbuilt automatic exposure control
adjustments. To analyze these algorithms, one needs to have an X-ray machine with access to the gateway of
network.
A graphical user interface (GUI) based LabVIEW approach is used to implement body mass index
calculations. If the body mass index is greater than the threshold, radiation dose increases. Caldose software
program is used to calculate radiation dose induced in different parts of the body for a specific X-ray profile.
Chest X-ray profile is selected for study because chest x ray is most frequently performed examination,
according to annual frequency of examination conducted during 2002 [8].
Different software’s were tried to reduce radiation dose. Some of them are Imasim, Mevislab,
Analyze 14.0, Code_Blocks, Git bash, medical image processing, analysis, and visualization (MIPAV),
DOSCTP/DOSXYZnrc. Many radiology-based apps were studied for the purpose of radiation dose reduction.
Software Techniques include removing noise in image by the use of various filters. As radiation is reduced
image becomes blurry. Hence it is ideal to minimize the noise in the X-ray image.
Data for analysis was collected from a private hospital. Two technicians were involved from
renowned healthcare technology management services. Inclusion criteria comprise general patients requiring
X-ray diagnosis. Oncology patients were excluded from the study. Statistical methods for noise analysis
include different types of filtering along with advanced image denoising algorithms.
3. TECHNIQUES IMPLEMENTED
3.1. Hardware techniques
Radiation exposure can be controlled by using hardware techniques. External parameters can be
varied for different setups for analysis purposes. LabVIEW and Caldose software were used for
implementation. Different methods of hardware techniques tried is as follows:
3.1.1. White paper of top medical device manufacturing company
According to medical device manufacturing company’s white paper [9], Radiation dose is calculated
based on patient size, density of anatomical area irradiated and C-arm regulation of x ray tube. Radiation
dose also depends on Tube voltage (kV), Tube current (mA), Exposure time (mS), Focal spot and pre-filter
(CARE filter).
As per the analysis, radiation dose cannot be reduced in already deployed machines. Image
processing was done using C++. Objective of research mainly deals with hardware [10] control. Radiation
builds on a patient body mass index. Radiograph energy is directly proportional to patient thickness. Reputed
medical device manufacturing company has inbuilt automatic exposure control adjustments. To analyze these
algorithms, one needs to have an X-ray machine with access to the gateway of network.
3.1.2. LabVIEW
Implementation of body mass index (BMI): Radiation dose depends on patient thickness.
Calculation of BMI for pediatric patients is implemented using LabVIEW as per Figure 2. Radiation dose
increases with BMI. Radiation dose needs to be adjusted with BMI. Implementation of radiation reduction
was tried in LabVIEW. Three hardware parameters were considered namely tube voltage, tube current and
exposure time. Hounsfield units using the (1).
Figure 2. Implementation of BMI using LabVIEW
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𝐻𝑈 =
(𝜇𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙−𝜇𝑤𝑎𝑡𝑒𝑟)
(𝜇𝑤𝑎𝑡𝑒𝑟)
× 100 (1)
Different substances like air, fat, soft tissue and bone were implemented using case structures.
Figure 3 shows an approach for radiation reduction-Hounsfield scale, using LabVIEW. But implementation
was misfired.
Figure 3. Radiation reduction using in LabVIEW using Hounsfield scale: declined
3.1.3. CalDose
CALDose_X 5.0 is the current version of a software program for X-ray diagnosis. It is used for
calculating organ absorbed doses and radiological risks. Figure 4 shows two human adult phantoms male
adult mesh (MASH) and female adult mesh (FASH). Clean and clear concept of radiation dose reduction is
implemented using Caldose. Figure 5 implies X-ray examination with different values of film-focus distance
(FFD), kv and mAs.
Figure 4. Two human adult phantoms
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Figure 5. X ray examination with FFD, mAs and kV
3.2. Software techniques
As radiation is reduced the image becomes blurry [11]. Radiologists find it difficult to diagnose the
complications in the patient’s X-ray. Because of hardware restrictions and practical issues, it is impossible to
reduce dose. Low dose CT [12] with image clarity is a challenge. Aim is to identify different noise in X-ray
image, reduce noise in image and enhance image characteristics using different image processing techniques [13].
Model based Iterative reconstruction techniques [14] with high performance [15] and graphics
processing unit [16] are some of the reconstruction [17] techniques recently used for chest X-ray used in
medical imaging [18]. Filtered back projection [19] technique solves the issues of blurring. Deep learning
methods [20] can be used for image reconstruction. Convolutional neural networks play an important role in
radiation reduction [21].
The design is based profoundly on the seed particle. By dividing the image in various fragments,
each and every single pixel value of the image is contrasted with respect to a particular threshold value
determined in consideration to the gray value of the seed point [22].
a. Take a pixel in the initial input image and specify it as a seed particle. Place the particular seed pixel
value toward a blank progression.
b. Since the beginning of the progression, find consequent 8- associated neighbors of every pixel which are
not processed and for every neighbor point, see if the gray zone value of that neighbor particle is in the
range of the stated discrepancy from the seed particle’s gray zone value. This discrepancy is illustrated
by (2).
(𝑓(𝑥,𝑦)−𝑠𝑒𝑒𝑑)
𝑠𝑒𝑒𝑑
<= £ (2)
Here, f (x, y) is the gray zonal value of the current particle and £ is the origin value equal to 0.5.
c. Thirdly, the foreground section is further improved by adjusting the histogram accordingly and is
summed up to the background section.
d. Lastly, an improved image is produced by combining the grade of the initial image to the image obtained
in the C step.
e. On the basis of the seed point which is taken, the entire image is divided into foreground and background
sections.
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4. RESULTS AND DISCUSSION
4.1. Results of hardware approach
Figure 6 gives a plot of potential versus air kerma. Figure 7 shows the beam position of chest
X-ray. Air kerma also increases with Potential. X-ray source reaches the detector through the sample.
Figure 6. Plot of potential versus air kerma
Figure 7. Beam position of chest X-ray
Figure 8 shows the dose analysis chart for FFD=220 cm, tube voltage = 150 kV and tube current and
exposure time product value 24 mAs. Figure 9 demonstrates dose analysis chart for variations in FFD.
Absorbed dose is calculated for different organs. X-ray parameters are varied for analysis purpose.
Figure 10 shows dose comparison chart with min and max FFD, constant mAs and kV. Figure 11
implies a dose comparison chart for min and max values of FFD, mAs and kV. Figure 12 dose comparison
with the variation of FFD, mAs and kV. Absorbed dose is calculated for different organs. X-ray parameters
are varied for analysis purpose.
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Figure 8. Dose analysis chart for FFD=220 cm, tube voltage = 150 kV and tube current and exposure time
product value 24 mAs
Figure 9. Dose analysis chart for variations in FFD
Figure 10. Dose comparison chart with min and max FFD, constant mAs and kV
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Figure 11. Dose comparison chart for min and max values of FFD, mAs and kV
Figure 12 Dose Comparison with the variation of FFD, mAs and kV
4.2. Results of software approach
Software approaches were divided into 3 cases. Case 1 was analyzed when X-ray is contaminated
with Gaussian noise. Figure 13 shows the input X-ray image contaminated with Gaussian noise. Figure 14
shows the output of the filter. Figure 15 shows an enhanced output image. Case 2 was analyzed when X-ray
is contaminated with salt and pepper noise. Figure 16 shows the input X-ray image contaminated with salt
and pepper noise. Figure 17 shows the output of the filter. Figure 18 shows an enhanced output image. Case 3
was analyzed when X-ray is contaminated with both Gaussian and salt and pepper interference. Figure 19
shows the input X-ray image contaminated with both Gaussian and salt and pepper interference. Figure 20
shows the output of the filter. Figure 21 shows an enhanced output image.
The innovative noise reduction technique which involves median filtering along with threshold
filtering obtained greater signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) values but,
however lower mean squared error (MSE) value [23]. This indicates that the novel interference reducing
algorithm which diminishes the interference than traditional procedures still maintain better visual details.
The human conception is no longer treated as a standard for the image quality, and therefore to estimate the
conduct of the proposed design, quality parameters were measured such as contrast to noise ratio (CNR),
SNR, PSNR, and MSE.
In hardware techniques, as focus to detector distance is increased dosage is reduced. Tube voltage,
tube current and exposure time [24] product value has to be minimum. Different algorithms can be used for
fast image processing, [25] which can be used for fluoroscopic applications [26]. Various CT reconstruction
[27] techniques may contribute to future work.
Dose analysis for FFD=120 cm, charge=10 mAs, voltage=60 kV
and FFD=220 cm, charge=24 mAs, voltage=150 kV
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CASE 1: when the X-ray is contaminated with Gaussian noise
Figure 13. Input X-ray image
contaminated with Gaussian noise
Figure 14. Filtered radiograph
image
Figure 15. Filtered and enhanced
output radiograph image
CASE 2: when the X-ray is corrupted with salt and pepper noise
Figure 16. Input X-ray image
corrupted with salt and pepper
interference
Figure 17. Filtered radiograph
image
Figure 18. Filtered and enhanced
output radiograph image
CASE 3: when the radiograph is contaminated with both Gaussian and salt and pepper interference
Figure 19. Input radiograph image
corrupted with both Gaussian, and
Salt and Pepper interference
Figure 20. Filtered radiograph
image
Figure 21. Filtered and enhanced
output X-ray image
5. CONCLUSION
Hardware Techniques implies that radiation dose can be reduced by increasing focus to detector
distance. Tube voltage, Tube current and exposure time product value has to be minimum and ensure quality
image is retained. Future work deals with the study of copper filters. Aluminum filters are used in most of the
X-ray machines. Copper filters reduce the radiation dose. In software techniques, the originally acquired
X-ray images are contaminated with noise due to the fact of instability in the case of sensors, electrical power
or X-ray source, that is responsible for the degradation of the image attributes such as submerging valuable
information, and blurring edges. Here an enhanced image denoising algorithm has been proposed which
decreases Gaussian noise combined with salt and pepper noise that retains most information particulars.
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In comparison of this method with the currently prevalent methods of flexible improvement and fine
stretching and other contrast enhancement techniques, it is summarized that this innovative technique is
exhibiting much better and improved outcomes than the currently prevalent ones. Moreover, the method is
seed-dependent and therefore the determination of seed particles is very much vital in this design. A seed
selected in dark sections will yield improved outcomes compared to the seed selected in the bright sections,
as it is expected that the enhancement of the darker regions of the image is required.
Also, the innovative enhancement technique can also be combined with adaptive enhancement
Method to produce outputs with still better results and improved visual perception Hard problems include
radiation dose reduction by adjusting the X-ray machine settings. Adjusting the settings of an X-ray machine
is a challenge. This has to be done by the X-ray machine manufacturers. Non obvious mistakes include
collection of data from the hospital instead of contacting the technician of the X-ray machine.
ACKNOWLEDGEMENTS
Author thanks guide Dr. Dayakshini, Head of ECE Department, Dr. Rio D Souza, Principal, and
St Joseph Engineering College for their guidance during my research work. Author would like to express
sincere gratitude to the management of St Joseph Engineering College for constant support.
REFERENCES
[1] ITN, “Radiology imaging, imaging technology news,” Imaging Technology News, https://www.itnonline.com/channel/radiology-
imaging/Radiation (accessed Dec. 12, 2020).
[2] FDA, “Computed tomography, US food drug administration.” US Food and Drug Administration. https://www.fda.gov/radiation-
emitting-products/medical-imaging/medical-x-ray-imaging (accessed Aug. 08, 2020).
[3] L. Perillo, G. Padricelli, G. Isola, F. Femiano, P. Chiodini, and G. Matarese, “Class II malocclusion division 1: a new
classification method by cephalometric analysis,” European Journal of Paediatric Dentistry, vol. 13, no. 3, pp. 192–196, 2012.
[4] G. Isola, L. Ramaglia, G. Cordasco, A. Lucchese, L. Fiorillo, and G. Matarese, “The effect of a functional appliance in the
management of temporomandibular joint disorders in patients with juvenile idiopathic arthritis,” Minerva Stomatologica, vol. 66,
no. 1, pp. 1–8, 2017, doi: 10.23736/S0926-4970.16.03995-3.
[5] L. Perillo, G. Isola, D. Esercizio, M. Iovane, G. Triolo, and G. Matarese, “Differences in craniofacial characteristics in Southern
Italian children from Naples: a retrospective study by cephalometric analysis,” European Journal of Paediatric Dentistry, vol. 14,
no. 3, pp. 195–198, 2013.
[6] A. Berrington de González, “Projected cancer risks from computed tomographic scans performed in the United States in 2007,”
Archives of Internal Medicine, vol. 169, no. 22, pp. 2071–2077, Dec. 2009, doi: 10.1001/archinternmed.2009.440.
[7] UT Southwestern Medical Center, “Diagnostic X-ray procedures.” utswmed.org, https://utswmed.org/conditions-
treatments/diagnostic-x-ray-procedures/ (accessed Jul. 03, 2020).
[8] M. B. Freitas, “Dose measurements in chest diagnostic X rays: adult and paediatric patients,” Radiation Protection Dosimetry,
vol. 111, no. 1, pp. 73–76, Aug. 2004, doi: 10.1093/rpd/nch363.
[9] Siemens-Healthineers, “AX care clear white paper low dose.” https://www.siemens-healthineers.com/it/angio/care-clear (accessed
Nov. 02, 2020).
[10] A. Sodickson, “Strategies for reducing radiation exposure from multidetector computed tomography in the acute care setting,”
Canadian Association of Radiologists Journal, vol. 64, no. 2, pp. 119–129, May 2013, doi: 10.1016/j.carj.2013.01.002.
[11] T. ten Cate et al., “Novel X-ray image noise reduction technology reduces patient radiation dose while maintaining image quality
in coronary angiography,” Netherlands Heart Journal, vol. 23, no. 11, pp. 525–530, Nov. 2015, doi: 10.1007/s12471-015-0742-1.
[12] K. P. Murphy et al., “Feasibility of low-dose CT with model-based iterative image reconstruction in follow-up of patients with
testicular cancer,” European Journal of Radiology Open, vol. 3, pp. 38–45, 2016, doi: 10.1016/j.ejro.2016.01.002.
[13] B. Luo, Z. Sun, M. Xue, and H. Liu, “Improved noise reduction algorithms for medical X-ray images,” in 2013 3rd International
Conference on Consumer Electronics, Communications and Networks, 2013, pp. 359–362, doi: 10.1109/CECNet.2013.6703346.
[14] P. J. Pickhardt et al., “Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial
comparing ultralow-dose with standard-dose imaging,” American Journal of Roentgenology, vol. 199, no. 6, pp. 1266–1274, Dec.
2012, doi: 10.2214/AJR.12.9382.
[15] X. Wang, A. Sabne, S. Kisner, A. Raghunathan, C. Bouman, and S. Midkiff, “High performance model based image
reconstruction,” in Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Feb.
2016, pp. 1–12, doi: 10.1145/2851141.2851163.
[16] A. Sabne, X. Wang, S. J. Kisner, C. A. Bouman, A. Raghunathan, and S. P. Midkiff, “Model-based iterative CT image
reconstruction on GPUs,” in Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel
Programming, Jan. 2017, pp. 207–220, doi: 10.1145/3018743.3018765.
[17] M. Katsura et al., “Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the
adaptive statistical iterative reconstruction technique,” European Radiology, vol. 22, no. 8, pp. 1613–1623, Aug. 2012, doi:
10.1007/s00330-012-2452-z.
[18] H. Scheffel et al., “Coronary artery plaques: Cardiac CT with model-based and adaptive-statistical iterative reconstruction
technique,” European Journal of Radiology, vol. 81, no. 3, pp. e363–e369, Mar. 2012, doi: 10.1016/j.ejrad.2011.11.051.
[19] Z. Deák et al., “Filtered back projection, adaptive statistical iterative reconstruction, and a model-based Iterative reconstruction in
abdominal CT: An experimental clinical study,” Radiology, vol. 266, no. 1, pp. 197–206, Jan. 2013, doi:
10.1148/radiol.12112707.
[20] J. Cheng et al., “Model-based deep medical Imaging: the roadmap of generalizing iterative reconstruction model using deep
learning,” arXiv:1906.08143, Jun. 2019.
[21] H. Chen et al., “Low-dose CT with a residual encoder-decoder convolutional neural network,” IEEE Transactions on Medical
Imaging, vol. 36, no. 12, pp. 2524–2535, Dec. 2017, doi: 10.1109/TMI.2017.2715284.
11. Int J Elec & Comp Eng ISSN: 2088-8708
An approach for radiation dose reduction in computerized tomography (Shama Bekal Narayan)
1179
[22] N. Kanwal, A. Girdhar, and S. Gupta, “Region based adaptive contrast enhancement of medical X-ray images,” in 2011 5th
International Conference on Bioinformatics and Biomedical Engineering, May 2011, pp. 1–5, doi: 10.1109/icbbe.2011.5780221.
[23] A. Neroladaki, D. Botsikas, S. Boudabbous, C. D. Becker, and X. Montet, “Computed tomography of the chest with model-based
iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations,” European
Radiology, vol. 23, no. 2, pp. 360–366, Feb. 2013, doi: 10.1007/s00330-012-2627-7.
[24] D. T. Raju and K. Shanthi, “Analysis on x-ray parameters of exposure by measuring x-ray tube voltage and time of exposure,”
The International Journal of Engineering and Science (IJES), vol. 3, no. 6, pp. 69–73, 2014.
[25] H. Nien and J. A. Fessler, “Relaxed linearized algorithms for faster X-ray CT image reconstruction,” IEEE Transactions on
Medical Imaging, vol. 35, no. 4, pp. 1090–1098, Apr. 2016, doi: 10.1109/TMI.2015.2508780.
[26] A. Qadir, “Fluoroscopy dose management.” https://www.slideshare.net/airwave12/patient-radiation-dose-management (accessed
May 18, 2020).
[27] J. Zhang, Y. Hu, J. Yang, Y. Chen, J.-L. Coatrieux, and L. Luo, “Sparse-view X-ray CT reconstruction with gamma
regularization,” Neurocomputing, vol. 230, pp. 251–269, Mar. 2017, doi: 10.1016/j.neucom.2016.12.019.
BIOGRAPHIES OF AUTHORS
Shama Bekal Narayan received the B.Eng. degree in Electronics and
communication engineering from A.P.S Engineering college, Bangalore, in 2004 and the
M.Tech from N.M.A.M. Institute of Technology (NMAMIT) Nitte and pursuing Ph.D. degree
from VTU, Belagavi. Currently, she is an Assistant Professor at the Department of Electronics
and communication Engineering, St Joseph Engineering College, Mangalore, Karnataka, India.
Her research interests include Biomedical image processing, digital circuits, wireless
communication, computer communication networks, digital image processing. She can be
contacted at shamabn@sjec.ac.in, ResearchGate: https://www.researchgate.net/profile/Shama-
Bekal.
Savitha Halkare Mahabaleshwara received her B.E. degree in Electronics and
Communication Engineering from Mysore University and her M. Tech. degree in Digital
Electronics and Communication from Visvesvaraya Technological University (VTU),
Belagavi. She was awarded Doctorate from National Institute of Technology Karnataka
(NITK), Surathkal, during April 2014. She has 26 years of teaching experience. She has
published around 25 research papers in international journals, international/National
conference Proceedings. At savitha100@gmail.com.