This summary provides the high level information from the document in 3 sentences:
The document presents an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical pathological images. ANN-C3 performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification of cells using a neural network. The system was able to accurately segment and classify cancerous versus non-cancerous cells in pathological images when compared to manual methods.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Classification of Osteoporosis using Fractal Texture FeaturesIJMTST Journal
In our proposed method an automatic Osteoporosis classification system is developed. The input of the system is Lumbar spine digital radiograph, which is subjected to pre-processing which includes conversion of grayscale image to binary image and enhancement using Contrast Limited Adaptive Histogram Equalization technique(CLAHE). Further Fractal Texture features(SFTA) are extracted, then the image is classified as Osteoporosis, Osteopenia and Normal using a Probabilistic Neural Network(PNN). A total of 158 images have been used, out of which 86 images are used for training the network and 32 images for testing and 40 images for validation. The network is evaluated using a confusion matrix and evaluation parameters like Sensitivity, Specificity, precision and Accuracy are computed fractal feature extraction techniques.
HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENTijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image
segmentation plays a crucial role in many medical imaging applications. There are many algorithms and
techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in
high resolution image for image segmentation due to variability of spectral and structural information.
Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for
segmentation of high resolution medical image is an efficient image segmentation technique. The proposed
method is implemented in Matlab and verified using various kinds of high resolution medical images. The
experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
Utilization of Super Pixel Based Microarray Image Segmentationijtsrd
In the division of PC vision pictures, Super pixels are go probably as key part from 10 years prior. There are various counts and methodology to separate the Super pixels anyway whole all of them the best super pixel looking at strategy is Simple Linear Iterative Clustering SLIC have come to pivot continuously recently. The concentrating of small scale group quality verbalization from MRI imaging is more useful to perceive tumors or some other dangerous development contaminations, so the fundamental DNA cDNA microarray is a grounded device for analyzing the same. The division of microarray pictures is the essential development in a microarray assessment. In this paper, we proposed a figuring to dividing the cDNA small show picture using Simple Linear Iterative Clustering SLIC based Self Organizing Maps SOM method. In any case, the proposed figuring is taken up a moving task to look at the bad quality of pictures in addition. There are two phases to separate the image, introductory, a pre setting up the applied picture to diminish fuss levels and second, to piece the image using SLIC based SOM approach. Mr. Davu Manikanta | Mr. Parasurama N | K Keerthi "Utilization of Super Pixel Based Microarray Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46274.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/46274/utilization-of-super-pixel-based-microarray-image-segmentation/mr-davu-manikanta
Performance Evaluation of Basic Segmented Algorithms for Brain Tumor DetectionIOSR Journals
In the field of computers segmentation of image plays a very important role. By this method the required
portion of object is traced from the image. In medical image segmentation, clustering is very famous
method . By clustering, an image is divided into a number of various groups or can also be called as clusters.
There are various methods of clustering and thresholding which have been proposed in this paper such as otsu
, region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means
(FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method
(developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As
process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and
performance parameters the segmentation of hierarchical self organizing mapping method is done in a better
way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce)
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
Classification of Osteoporosis using Fractal Texture FeaturesIJMTST Journal
In our proposed method an automatic Osteoporosis classification system is developed. The input of the system is Lumbar spine digital radiograph, which is subjected to pre-processing which includes conversion of grayscale image to binary image and enhancement using Contrast Limited Adaptive Histogram Equalization technique(CLAHE). Further Fractal Texture features(SFTA) are extracted, then the image is classified as Osteoporosis, Osteopenia and Normal using a Probabilistic Neural Network(PNN). A total of 158 images have been used, out of which 86 images are used for training the network and 32 images for testing and 40 images for validation. The network is evaluated using a confusion matrix and evaluation parameters like Sensitivity, Specificity, precision and Accuracy are computed fractal feature extraction techniques.
HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENTijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image
segmentation plays a crucial role in many medical imaging applications. There are many algorithms and
techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in
high resolution image for image segmentation due to variability of spectral and structural information.
Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for
segmentation of high resolution medical image is an efficient image segmentation technique. The proposed
method is implemented in Matlab and verified using various kinds of high resolution medical images. The
experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
Utilization of Super Pixel Based Microarray Image Segmentationijtsrd
In the division of PC vision pictures, Super pixels are go probably as key part from 10 years prior. There are various counts and methodology to separate the Super pixels anyway whole all of them the best super pixel looking at strategy is Simple Linear Iterative Clustering SLIC have come to pivot continuously recently. The concentrating of small scale group quality verbalization from MRI imaging is more useful to perceive tumors or some other dangerous development contaminations, so the fundamental DNA cDNA microarray is a grounded device for analyzing the same. The division of microarray pictures is the essential development in a microarray assessment. In this paper, we proposed a figuring to dividing the cDNA small show picture using Simple Linear Iterative Clustering SLIC based Self Organizing Maps SOM method. In any case, the proposed figuring is taken up a moving task to look at the bad quality of pictures in addition. There are two phases to separate the image, introductory, a pre setting up the applied picture to diminish fuss levels and second, to piece the image using SLIC based SOM approach. Mr. Davu Manikanta | Mr. Parasurama N | K Keerthi "Utilization of Super Pixel Based Microarray Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46274.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/46274/utilization-of-super-pixel-based-microarray-image-segmentation/mr-davu-manikanta
Performance Evaluation of Basic Segmented Algorithms for Brain Tumor DetectionIOSR Journals
In the field of computers segmentation of image plays a very important role. By this method the required
portion of object is traced from the image. In medical image segmentation, clustering is very famous
method . By clustering, an image is divided into a number of various groups or can also be called as clusters.
There are various methods of clustering and thresholding which have been proposed in this paper such as otsu
, region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means
(FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method
(developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As
process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and
performance parameters the segmentation of hierarchical self organizing mapping method is done in a better
way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce)
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
View classification of medical x ray images using pnn classifier, decision tr...eSAT Journals
Abstract: In this era of electronic advancements in the field of medical image processing, the quantum of medical X-ray images so produced exorbitantly can be effectively addressed by means of automated indexing, comparing, analysing and annotating that will really be pivotal to the radiologists in interpreting and diagnosing the diseases. In order to envisage such an objective, it has been humbly endeavoured in this paper by proposing an efficient methodology that takes care of the view classification of the X-ray images for the automated annotation from their vast database, with which the decision making for the physicians and radiologists becomes simpler despite an immeasurable and ever-growing trends of the X-ray images. In this paper, X-ray images of six different classes namely chest, head, foot, palm, spine and neck have been collected. The framework proposed in this paper involves the following: The images are pre-processed using M3 filter and segmentation by Expectation Maximization (EM) algorithm, followed by feature extraction through Discrete Wavelet Transform. The orientation of X-ray images has been performed in this work by comparing among the Probabilistic Neural Network (PNN), Decision Tree algorithm and Support Vector Machine (SVM), while the PNN yields an accuracy of 75%, the Decision Tree with 92.77% and the SVM of 93.33%. Key Words: M3 filter, Expectation Maximaization, Discrete Wavelet Transformation, Probabilistic Neural Network, Decision Tree Algorithm and Support Vector Machine.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Performance Evaluation of Basic Segmented Algorithms for Brain Tumor DetectionIOSR Journals
Abstract: In the field of computers segmentation of image plays a very important role. By this method the re-quired portion of object is traced from the image. In medical image segmentation, clustering is very famous method . By clustering, an image is divided into a number of various groups or can also be called as clusters. There are various methods of clustering and thresholding which have been proposed in this paper such as otsu , region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and performance parameters the segmentation of hierarchical self organizing mapping method is done in a better way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce) . Keywords - area (A), Fuzzy C means, global consistency error (Gce) , HSOM, K means , Otsu , rand index (Ri), Region Growing , segmentation accuracy (Sa) , and variation of information (Vi).
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
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Hybrid Approach for Brain Tumour Detection in Image Segmentationijtsrd
In this paper we have considered illustrating a few techniques. But the numbers of techniques are so large they cannot be all addressed. Image segmentation forms the basics of pattern recognition and scene analysis problems. The segmentation techniques are numerous in number but the choice of one technique over the other depends only on the application or requirements of the problem that is being considered. Analysis of cluster is a descriptive assignment that perceive homogenous group of objects and it is also one of the fundamental analytical method in facts mining. The main idea of this is to present facts about brain tumour detection system and various data mining methods used in this system. This is focuses on scalable data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data power to perform complex transformations and analysis will be measured between the way of measuring the Furrier and Wavelet Transform distance. Sandeep | Jyoti Kataria "Hybrid Approach for Brain Tumour Detection in Image Segmentation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33409.pdf Paper Url: https://www.ijtsrd.com/medicine/other/33409/hybrid-approach-for-brain-tumour-detection-in-image-segmentation/sandeep
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
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11.artificial neural network based cancer cell classification
1. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol 3, No.2, 2012
Artificial Neural Network based Cancer Cell Classification
(ANN – C3)
Guruprasad Bhat1* Vidyadevi G Biradar2 H Sarojadevi2 Nalini N2
1. Cisco Systems, SEZ Unit, Cessna business park, Marathahalli-Sarjapur outer ring road, Bangalore,
Karnataka, India – 560 103
2. Nitte Meenakshi Insitute of Technology, Bangalore , Karnataka, India – 560 064
*guruprasadbharatibhat@gmail.com,
vgb2011@gmail.com,hsarojadevi@gmail.com,nalinaniranjan@hotmail.com
Abstract
This paper addresses the system which achieves auto-segmentation and cell characterization for prediction
of percentage of carcinoma (cancerous) cells in the given image with high accuracy. The system has been
designed and developed for analysis of medical pathological images based on hybridization of syntactic
and statistical approaches, using Artificial Neural Network as a classifier tool (ANN) [2]. This system
performs segmentation and classification as is done in human vision system [1] [9] [10] [12], which
recognize objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by
texture information and brightness.
In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing
texture-primitive features and segmentation with Artificial Neural Network (ANN) classifier tool. The
present approach directly combines second, third, and fourth steps into one algorithm. This is a semi-
supervised approach in which supervision is involved only at the level of defining structure of Artificial
Neural Network; afterwards, algorithm itself scans the whole image and performs the segmentation and
classification in unsupervised mode. Finally, algorithm was applied to selected pathological images for
segmentation and classification. Results were in agreement with those with manual segmentation and were
clinically correlated [18] [21].
Keywords: Grey scale images, Histogram equalization, Gausian filtering, Haris corner detector, Threshold,
Seed point, Region growing segmentation, Tamura texture feature extraction, Artificial Neural
Network(ANN), Artificial Neuron, Synapses, Weights, Activation function, Learning function,
Classification matrix.
1. Introduction
In the modern age of computerized fully automated trend of living, the field of automated diagnostic
systems plays an important and vital role. Automated diagnostic system designs in Medical Image
processing are one such field where numerous systems are proposed and still many more under conceptual
design due explosive growth of the technology today. From the past decades, we have witnessed an
explosive growth of Digital image processing for analysis of the data that can be captured by digital images
and artificial neural networks are used to aggregate the analyzed data from these images to produce a
diagnosis prediction with high accuracy instantaneously where digital images serve as tool for input data
[20] [21]. Hence in the process of surgery these automated systems help the surgeon to identify the infected
parts or tumors in case of cancerous growth of cells to be removed with high accuracy hence by increasing
the probability of survival of a patient. In this proposal one of such an automated system for cancer cell
classification which helps as a tool assisting surgeon to differentiate cancerous cells from those normal
cells i.e. percentage of carcinoma cells, instantaneously during the surgery. Here the pathological images
serve as input data. The analysis of these pathological images is directly based on four steps: 1) image
filtering or enhancement, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by
pattern recognition system or classifier [21]. Since neural network ensembles are used as decision makers
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even though network takes more time to adapt behavior, once it is trained it classifies almost
instantaneously due to electrical signal communication of nodes in the network.
2. System architecture
The ANN – C3 architecture is shown in figure 1. It comprises of five distinct components, as show below.
Each component is described briefly in subsequent sections.
Figure 1: ANN - C3 system architecture
2.1 Images used
This system is designed and verified to take grey scale pathological images as input. Grey scale
pathological images help to identify affected cells makes these images for analysis of cancerous growth of
cells.
2.2 Pre-processing
Grey scale pathological imaging process may be dirtied by various noises. Perform an image pre processing
task to remove noise in a pathological image first. To remove the noise the Histogram equalization or
Gaussian filter based median filtering is done [5] [6] [8] [19].
2.3 Segmentation
Segmentation includes two phases. First phase deals with threshold detection and the later one with similar
region identification. For threshold detection various methods like GUI selection, graphical method or
corner detectors can be used. GUI selection reduces automation and graphical method fails when multiple
objects are present in an input data. Since this design mainly deals with multiple objects (cells) in an input
image, Haris corner detectors are used to find threshold. In second phase, threshold points detected by
corners serve as seed point for segmentation. Four neighborhood based region growing segmentation is
used increase the speed compare to eight neighborhood and increase the accuracy compared to region split
and merge i.e. trade off between accuracy and speed. A brief discussion of Haris corner detector and 4-
neighborhood region growing Segmentation is done in section III [11] [13].
2.4 Feature Extraction
Neural network classifiers are those differ from traditional classifiers like Bayesian and k – nearest
neighborhood classifiers in various aspects from type of input data to output representation. Since the
neural networks are used as classifiers in this design which takes only numerical data as input rather than
any kind of data as input by Bayesian and k – nearest neighbor classifiers, the input image data has to be
converted to numerical form. This conversion is done by extracting tamura texture features. A brief
discussion of tamura texture feature is done in section IV.
Tamura texture features:
The human vision system (HVS) permits scene interpretation ‘at a glance’ i.e. the human eye ‘sees’ not
scenes but sets of objects in various relations to each other, in spite of the fact that the ambient illumination
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is likely to vary from one object to another—and over the various surfaces of each object—and in spite of
the fact that there will be secondary illumination from one object to another. These variations in the
captured images are referred as tamura texture features, even the same texture features are observed by
surgeon to differentiate carcinoma cells and non-carcinoma cells.
2.5 Neural Network
Supervised feed-forward back-propagation neural network ensemble used as a classifier tool. As discussed
previously, neural network differs in various ways from traditional classifiers like Bayesian and k – nearest
neighbor classifiers. One of the main differences is linearity of data. Traditional classifiers like Bayesian
and k – nearest neighbor requires linear data to work correctly. But neural network works as well for non-
linear data because it is simulated on the observation of biological neurons and network of neurons. Wide
range of input data for training makes neural network to work with higher accuracy, in other words a small
set of data or large set of similar data makes system to be biased [22]. Thus neural network classifier
requires a large set of data for training and also long time to train to reach the stable state. But once the
network is trained it works as fast as biological neural networks by propagating signals as fast as electrical
signals.
3. Haris corner detector and 4-neighborhood region growing segmentation
3.1 Haris corner detector
A corner can be defined as the intersection of two edges. A corner can also be defined as points for which
there are two dominant and different edge directions in a local neighborhood of the point. An interest point
is a point in an image which has a well-defined position and can be robustly detected. This means that an
interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum
or minimum, line endings, or a point on a curve where the curvature is locally maximal.
In practice, most so-called corner detection methods detect interest points in general, rather than corners in
particular. As a consequence, if only corners are to be detected it is necessary to do a local analysis of
detected interest points to determine which of these real corners are.
A simple approach to corner detection in images is using correlation, but this gets very computationally
expensive and suboptimal. Haris corner detector is one such corner detector, which uses differential of the
corner score with respect to direction directly, instead of using shifted patches. This corner score is often
referred to as autocorrelation.
The algorithm of haris corner detector as follows:
Without loss of generality, we will assume a grayscale 2-dimensional image is used. Let this image be
given by I. Consider taking an image patch over the area (u,v) and shifting it by (x,y). The weighted sum of
squared differences (SSD) between these two patches, denoted S, is given by:
(1)
I(u + x,v + y) can be approximated by a Taylor expansion . Let Ix and Iy be the partial derivatives of I, such
that
(2)
This produces the approximation
(3)
This can be written in matrix form:
(4)
Where A is the structure tensor,
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(5)
This matrix (5) is a Harris matrix, and angle brackets denote averaging (i.e. summation over (u,v)). If a
circular window is used, then the response will be isotropic [16].
A corner (or in general an interest point) is characterized by a large variation of S in all directions of the
vector (x,y). By analyzing the eigenvalues of A, this characterization can be expressed in the following
way: A should have two "large" eigenvalues for an interest point. Based on the magnitudes of the
eigenvalues, the following inferences can be made based on this argument:
If λ1≈0 and λ2≈0 then this pixel (x , y) has no features of interest.
If λ1≈0 and λ2 has some large positive value, then an edge is found.
If λ1 and λ2 have large positive values, then a corner is found.
Haris and Stephens noted that exact computation of the eigenvalues is computationally expensive, since it
requires the computation of a Square root, and instead suggest the following function Mc, where κ is a
tunable sensitivity parameter:
Mc= λ1λ2 – k(λ1+λ2)2=det(A) – k trace2(A) (6)
Therefore, the algorithm does not have to actually compute the Eigen value decomposition of the matrix A
and instead it is sufficient to evaluate the determinant and trace of A to find corners, or rather interest points
in general.
The value of κ has to be determined empirically, and in the literature values in the range 0.04 - 0.15 have
been reported as feasible.
The covariance matrix for the corner position is A − 1, i.e.
(7)
Compute x and y derivatives of image
(8)
(9)
Compute product of derivatives of each image
(10)
(11)
3. Compute the sums of products of derivatives at each pixel
(12)
(13)
(14)
Define at each pixel (x, y) the matrix
(15)
Compute the response of the detector at each pixel
R=Det(H) – k(Trace(H))2 (16)
Threshold on value R. Compute nonmax suppression.
3.2 4-neigbourhood region growing Segmentation
Segmentation is the process of identifying the region of interest from the input image. Considering an input
image I being read and converted to the greyscale image .let’s assume the seed point to be (x , y). If the
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seed point is provided by the GUI then a function getpts() will make sure the x and y axes values have been
fetched. To create a mask we’ll convert all the pixels in the image I’ to 0 and call the image J.
In order to discover the neighbors we will use four pixel connectivity [14]. Starting with the seed point the
algorithm looks for the 4 pixels surrounding the pixel in consideration. Every time a surrounding pixel is
considered, the region mean is calculated and checked with that of the pixel in consideration and added to
the region. Similarly as the pixel is added to the region corresponding pixel in the image J is highlighted to
1 which would result in the highest intensity hence illuminating the pixel. As the segmentation continues
the region into consideration is intensified in the image J resulting in the segmentation of the affected area,
which later can be combined with the original image and displayed to the user.
5. Tamura Textutre feature extraction
Tamura texture feature concepts proposed by Tamura et al in 1978. These tamura texture features
corresponding to human perception and these features examined by 6 different constituent features. Six
features are: [15]
Coarseness – Coarseness is the numerical value describing whether texture is coarse or fine.
Contrast – Contrast defines whether texture contrast is high or low.
Directionality – Directionality defines whether texture pallets are oriented in single direction or not i.e.
directional or non-directional.
Line-likeness – Line-likeness correspond to pattern elements i.e. whether texture formed by lines i.e. line-
like or blob-like.
Regularity – Regularity defines the interval in which patterns repeated. If patterns are repeated in regular
interval then the texture is regular else it is said to be Irregular.
Roughness – Roughness defines the whether the surface is rough or smooth.
In these six features, Coarseness, Contrast and Directionality correspond to strong human perception and
these features are calculated pixel-wise by creating 3-D histogram of these three features. Estimation of
these three features are described in subsequent sections.
Coarseness relates to distances of notable spatial variations of grey levels, that is, implicitly, to the size of
the primitive elements (texels) forming the texture. The proposed computational procedure accounts for
differences between the average signals for the non-overlapping windows of different size:
At each pixel (x,y), compute six averages for the windows of size 2k × 2k, k=0,1,...,5, around the pixel.
At each pixel, compute absolute differencesEk(x,y) between the pairs of nonoverlapping averages in the
horizontal and vertical directions.
At each pixel, find the value of k that maximises the difference Ek(x,y) in either direction and set the best
size Sbest(x,y)=2k.
Compute the coarseness feature Fcrs by averaging Sbest(x,y) over the entire image. Instead of the average of
Sbest(x,y, an improved coarseness feature to deal with textures having multiple coarseness properties is a
histogram characterising the whole distribution of the best sizes over the image.
Contrast measures how grey levels q; q = 0, 1, ..., qmax, vary in the image g and to what extent their
distribution is biased to black or white. The second-order and normalised fourth-order central moments of
the grey level histogram (empirical probability distribution), that is, the variance, σ2, and kurtosis, α4, are
used to define the contrast:
(17)
Where,
(18)
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(19)
(20)
and m is the mean grey level, i.e. the first order moment of the grey level probability distribution. The value
n=0.25 is recommended as the best for discriminating the textures.
Degree of directionality is measured using the frequency distribution of oriented local edges against their
directional angles. The edge strength e(x,y) and the directional angle a(x,y) are computed using the Sobel
edge detector approximating the pixel-wise x- and y-derivatives of the image:
e(x,y)=0.5(|∆x(x,y)|+ |∆y(x,y)|) (21)
-1
a(x,y)=tan (∆x(x,y)/ ∆y(x,y)) (22)
where ∆x(x,y) and ∆y(x,y) are the horizontal and vertical grey level differences between the neighbouring
pixels, respectively. The differences are measured using the following 3 × 3 moving window operators:
−1 0 1 1 1 1
−1 0 1 0 0 0
−1 0 1 −1 −1 −1
A histogram Hdir(a) of quantised direction values a is constructed by counting numbers of the edge pixels
with the corresponding directional angles and the edge strength greater than a predefined threshold. The
histogram is relatively uniform for images without strong orientation and exhibits peaks for highly
directional images. The degree of directionality relates to the sharpness of the peaks:
(23)
where np is the number of peaks, ap is the position of the pth peak, wp is the range of the angles attributed to
the pth peak (that is, the range between valleys around the peak), r denotes a normalising factor related to
quantising levels of the angles a, and a is the quantised directional angle (cyclically in modulo 180o). Three
other features are highly correlated with the above three features and do not add much to the effectiveness
of the texture description.
The linelikeness feature Flin is defined as an average coincidence of the edge directions (more precisely,
coded directional angles) that co-occurred in the pairs of pixels separated by a distance d along the edge
direction in every pixel. The edge strength is expected to be greater than a given threshold eliminating
trivial "weak" edges. The coincidence is measured by the cosine of difference between the angles, so that
the co-occurrences in the same direction are measured by +1 and those in the perpendicular directions by -
1. The regularity feature is defined as Freg=1-r(scrs+scon+sdir + slin) where r is a normalising factor and each
s... means the standard deviation of the corresponding feature F... in each subimage the texture is partitioned
into. The roughness feature is given by simply summing the coarseness and contrast measures:
Frgh=Fcrs+Fcon . These features capture the high-level perceptual attributes of a texture well and are useful
for image browsing. However, they are not very effective for finer texture discrimination.
6. Artificial Neural Network
A neural network is a massively parallel distributed processor that has a natural propensity for storing
experiential knowledge and making it available for use. It resembles the brain in two respects [3] [4] [7]:
1. Knowledge is acquired by the network through a learning process.
2. Interneuron connection strengths known as synaptic weights are used to store the knowledge.
Benefits of neural network
Nonlinearity.
Input-output mapping.
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Adaptivity.
Contextual information.
Fault tolerance.
VLSI implementability.
Uniformity of analysis and design.
Neurobiological analogy.
Model of a neuron
A neuron is an information-processing unit that is fundamental to the operation of a neural network. We
may identify three basic elements of the neuron model: [17] [18]
Figure 2:Non-linear model of a neuron.
A set of synapses, each of which is characterized by a weight or strength of its own. Specifically, a signal xj
at the input of synapse j connected to neuron k is multiplied by the synaptic weight wkj. It is important to
make a note of the manner in which the subscripts of the synaptic weight wkj are written. The first subscript
refers to the neuron in question and the second subscript refers to the input end of the synapse to which the
weight refers. The weight wkj is positive if the associated synapse is excitatory; it is negative if the synapse
is inhibitory.
An adder for summing the input signals, weighted by the respective synapses of the neuron.
An activation function for limiting the amplitude of the output of a neuron. The activation function is also
referred to in the literature as a squashing function in that it squashes (limits) the permissible amplitude
range of the output signal to some finite value.
Typically, the normalized amplitude range of the output of a neuron is written as the closed unit interval [0,
1] or alternatively [-1, 1].
The model of a neuron also includes an externally applied bias (threshold) wk0 = bk that has the effect of
lowering or increasing the net input of the activation function.
Since after feature tamura feature extraction data is in the form of numerical values, Artificial Neural
Network classifier suits well for classification. Also non – linearity of the data makes other traditional
classifiers like Bayesian and kth – nearest neighbor classifier inefficient compared to ANN classifier. Thus
in this system ANN classifier is used as classification tool.
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7. Experimental results
Figure 3: Intermediate result
Figure 3 shows the intermediate result after corner detection. First image in figure 3 is the input image, 2nd
image displayed is histogram equalized image. From this histogram equalized image threshold points
detected and marked with red + marks as shown in third image of figure3.
From each seed point region is extracted and from extracted region tamura features are calculated. Each
feature vector consists of 4 features and n number of such feature vectors can be obtained from single
image which helps to prevent the system to be biased.
Extracted feature vectors are sent to neural network. The performance measurement with variable number
of hidden layer neurons with single layered feed forward back- propagation network is tabulated in table1:
Index Number of Percentage of correct
neurons classification
1. 20 96.4286%
2. 21 85.7143%
3. 22 92.8571%
4. 23 92.8571%
5. 24 96.4286%
Table 1- variable number of hidden layer neurons
The performance measurement with variable number of hidden layers with fixed number of neurons of 20
neurons in each layer, feed forward back- propagation network is tabulated in following table:
Index Number of Percentage of correct
hidden layers classification
1. 1 96.4286%
2. 2 89.2857%
3. 3 85.7143%
Table 2- variable number of hidden layers
8. Conclusion
Even though there is no successful generalized neural network configuration, for a particular application
a neural network with acceptable level of accuracy can be designed by selecting suitable number of hidden
layers, number of neurons per hidden layer and transfer and learning functions. The performance also
depends on the training function parameters like whether it is a batch training or one input at a time. Also
we have witnessed the advantages of neural network classifiers over other traditional classifiers like
Bayesian and k – nearest neighbor classifiers.
This design can be extended to estimate the number of carcinoma cells per unit area. This estimation
14
9. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol 3, No.2, 2012
helps in automated diagnosis systems like blood purifier in case of blood cancer. Also this can extended to
take color image as input with more feature added to feature vector to increase the accuracy of the output.
References
[ 1]. “An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical
Images for Classification Learning”.
[ 2]. Basavaraj .S. Anami1 and Vishwanath.C.Burkpalli 2 1. Principal, K.L.E.Institute of Technology, Hubli-
580030, India 2. Research Scholar, Basaveshwar Engineering College, Bagalkot – 587102, India.
[ 3]. “Texture based Identification and Classification of Bulk Sugary Food Objects”, ICGST-GVIP Journal,
ISSN: 1687-398X, Volume 9, Issue 4, August 2009.
[ 4]. Bing Gong, School of Computer Science and Technology Heilongjiang University Harbin, China, “A
Novel Learning Algorithm of Back-propagation Neural Network” ,2009 IITA International Conference
on Control, Automation and Systems Engineering.
[ 5]. Weilin Li, Pan Fu and Weiqing Cao, “Tool Wear States Recognition Based on Genetic Algorithm and
Back Propagation Neural Network Model”, 2010 International Conference on Computer Application and
System Modeling (1CCASM 2010)
[ 6]. Acharya and Ray, “Image Processing: Principles and Applications”, Wiley-Interscience 2005 ISBN 0-
471-71998-6
[ 7]. Russ, “The Image Processing Handbook”, Fourth Edition, CRC 2002 ISBN 0-8493-2532-3
[ 8]. SIMON HAYKIN, Book on “Neural Networks”, 2nd edition, A comprehensive edition.
[ 9]. “Digital Image Processing” by Gonzalez & Woods 2nd edition
[ 10]. H. Tamura, S. Mori, and T. Yamawaki, "Texture features corresponding to visual perception," IEEE
Trans. On Systems, Man, and Cybernetics, vol. Smc-8, No. 6, June 1978.
[ 11]. J. Smith and S.-F. Chang, “Transform features for texture classification and discrimination in large image
database”.IEEE Intl. Conf. on Image Proc., 1994.
[ 12]. Castleman K R. “Digital image processing”. NJ: Prentice Hall, 1996.
[ 13]. Manjunath, B., Ma, W.: “Texture features for browsing and retrieval of image data”. IEEE Trans on
Pattern Analysis and Machine Intelligence 18 (1996) 837842
[ 14]. Alexander Suhre, A. Enis Cetin, Tulin Ersahin, Rengul Cetin-Atalay, “Classification of cell images using
a generalized harris Corner Detector”
[ 15]. R. Adams and L. Bischof, “Seeded Region Growing”, IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 16, pp. 641-647, 1994.
[ 16]. R. Haralick, “Statistical and structural approaches to texture”, Proceedings of the IEEE, vol. 67, pp. 786–
804, 1979.
[ 17]. C. Harris and M.J. Stephens. “A combined corner and edge detector”. In Alvey Vision Conference, pages
147–152, 1988. D.
[ 18]. Ballard and C. Brown, “Computer Vision”, Prentice-Hall, Inc., 1982, Chap. 6.
[ 19]. Davies, “Machine Vision: Theory”, Algorithms and Practicalities, Academic Press, 1990, Chap. 18.
[ 20]. A K Jain, “Fundamentals of Digital Image Processing”, Prentice-Hall, 1986, Chap. 9.
[ 21]. D. Vernon Zhi-Hua Zhou, Yuan Jiang, Yu-Bin Yang, Shi-Fu Chen , “Lung Cancer Cell Identification
Based on Artificial Neural Network Ensembles”, National Laboratory for Novel Software Technology,
Nanjing University, Nanjing 210093, P.R.China. Artificial Ingelligence in Medicine, 2002, vol.24, no.1,
pp.25-36. @Elsevier.
[ 22]. Leonard Fass , “Imaging and cancer: A review”, GE Healthcare, 352 Buckingham Avenue, Slough, SL1
4ER, UK Imperial College Department of Bioengineering, London, UK.
[ 23]. Jinggangshan, P. R. China , “Application of Neural Networks in Medical Image Processing” ISBN 978-
952-5726-09-1, Proceedings of the Second International Symposium on Networking and Network
Security (ISNNS ’10), 2-4, April. 2010, pp. 02.
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10. Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
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Figure 1: ANN - C3 system architecture
Index Number of Percentage of correct
neurons classification
1. 20 96.4286%
2. 21 85.7143%
3. 22 92.8571%
4. 23 92.8571%
5. 24 96.4286%
Table 1: Variable number of hidden layer neurons
Index Number of Percentage of correct
hidden layers classification
1. 1 96.4286%
2. 2 89.2857%
3. 3 85.7143%
Table 2: Variable number of hidden layers
16
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