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Mahalakshmi, D. M.
- Brain Tumour Segmentation Strategies Utilizing Mean Shift Clustering and Content based Active Contour Segmentation
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Authors
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1 Department of Electrical Engineering, PSG College of Technology, IN
1 Department of Electrical Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 4 (2019), Pagination: 2002-2008Abstract
This paper proposes an automatic brain tumor segmentation using Mean shift clustering and content based active contour segmentation. In diagnosis of the disease medical imaging has more advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A brain tumor occurs when abnormal cells form in the brain. A proper diagnosis of brain tumor is provided by the medical imaging. The detection of tumor from brain is an important and difficult task in the medical field. One essential part in detecting the tumor is image segmentation. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. In view of high amount information in MRI pictures, tumor segmentation and classification are hard. The image segmentation is performed on different dataset of MRI cerebrum tumor pictures. The segmentation gives an automatic brain tumor recognition method to build the exactness, yields with decline in the analysis time. The image segmentation technique includes image acquisition, image preprocessing, denoising, and finally the feature extraction. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it undergoes segmentation process, where Mean Shift Clustering and Content based active segmentation techniques are used. Finally, the features are extracted from the segmented image using gray level co-occurrence matrix (GLCM). The image segmentation is implemented using MATLAB software. Finally, the tumor is segmented and energy, contrast, correlation, homogeneity is extracted, and comparison results are analyzed.Keywords
Edge Adaptive Total Variation Denoising (EATVD), Gray Level Cooccurrence Matrix (GLCM), Magnetic Resonance Imaging (MRI), Convolution Neural Network (CNN), Artificial Neural Network (ANN), Support Vector Machine (SVM).References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, Alexandria Engineering Journal, Vol. 57, No. 4, pp. 2387-2392, 2018.
- T. Chithambaram and K. Perumal, “Brain tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 970-982, 2017.
- T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No. 3, pp. 15041510, 2017.
- P. Chinmayi, L. Agilandeeswari, M. Prabu Kumar and K. Muralibabu, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 151-160, 2017.
- G. Malyadri, K.L. Sravani and Jyothi Kavathi, “Brain Tumor Detection System for Health Monitoring”, International Journal of Pure and Applied Mathematics, Vol. 114, No. 10, pp. 103-108, 2017.
- P. Priyadarsni, B. Nandhini, A.R. Catherine, K. Sahana and K. Sundaravadivu, “Soft-Computing Assisted Tool to Extract Tumor Section from Brain MR Images”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 2776-2780, 2017.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the Help of KSVM”, International Journal of Engineering and Technical Research, Vol. 7, No. 2, pp. 2454-4698, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, IEEE International Conference on Computing for Sustainable Global Development, pp. 1628-1632, 2016.
- S. Shubhangi and Pradeep M. Patil, “Brain Tumor Classification using Artificial Neural Network on MRI Images”, International Journal of Research in Engineering and Technology, Vol. 2, No. 12, pp. 218- 226, 2015.
- K. Machhale, H.B. Nandpuru, V. Kapur and L. Kosta, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 60-65, 2015.
- P. Sangeetha, “Brain Tumor Classification using PNN and Clustering”, Proceedings of International Conference on Innovations in Engineering and Technology, Vol .3, No. 3, pp. 796-803, 2014.
- Snehal Basutkar, Aparna Davkhar, Bharat Mahajan and Moresh Mukhedkar, “Brain Tumor Detection using Segmentation”, International Journal of Advance Engineering and Research Development, Vol. 3, No. 3, pp. 682-687, 2016.
- D. Haritha, “Brain Tumor Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- Shubhangi S. Veer and Pradeep M. Patil, “Brain Tumor Segmentation using GLCM”, International Journal of Emerging Technologies and Engineering, Vol. 2, No. 9, pp. 131-135, 2015.
- Nameirakpam Dhanachandra, Khumanthem Manglem and Yambem JinaChanu, “Image Segmentation using K-means Clustering Algorithm and Subtractive Clustering Algorithm”, Proceedings of 11th International MultiConference on Information Processing, pp. 764-771, 2015.
- Shweta Jain, “Brain Cancer Classification using GLCM Based Feature Extraction in Artificial Neural Network”, International Journal of Computer Science and Engineering Technology, Vol. 4, No. 7, pp. 966-970, 2013.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM Based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Mathew, P.B. Anto and N.K. Thara, “Brain Tumor Segmentation and Classification using DWT, Gabour Wavelet and GLCM”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1744-1750, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour-based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- Q. Sun and H. Tian, “Interactive Image Segmentation using Power Watershed and Active Contour Model”, Proceedings of 3rd IEEE International Conference on Network Infrastructure and Digital Content, pp. 401-405, 2012.
- Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification
Abstract Views :144 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2145-2152Abstract
A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.Keywords
Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 3, pp. 666-671, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 1-6, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No 3, pp. 1-12, 2017, pp. 155-164.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the help of KSVM”, International Journal of Engineering and Technical Research, Vol.7, No. 2, pp. 31-39, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, Proceedings of IEEE International Conference on Computing for Sustainable Global Development, pp. 1-7, 2016.
- D. Haritha, “Brain Tumour Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM Based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Matthew, A. Prasad and P.B. Anto, “A Review on Feature Extraction Techniques for Tumour Detection and Classification from Brain MRI”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1766-1771, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- D.R. Byreddy and M. Raghunadh, “An Application of Geometric Active Contour in Bio-Medical Engineering”, Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, pp. 322-326, 2014.
- Ruchi D. Deshmukh and Chaya Jadhav, “Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology, Vol. 4, No. 4, pp. 133-136, 2014.
- A.R. Kavitha, L. Chitra and R. Kanaga, “Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 3, pp. 1468-1471, 2016.
- R.J. Ramteke and Y. Khachane Monali, “Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbor”, International Journal of Advanced Computer Research, Vol. 2, No. 4, pp. 190-196, 2012.
- Aurav Gupta and Vinay Singh, “Brain Tumor Segmentation and Classification using FCM and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.
- N. Vani, A. Sowmya and N. Jayamma, “Brain Tumor Classification using Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.
- S.U Aswathy, G. Glan Devadhas and S.S. Kumar, “MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.
- Narmada M. Balasooriya and Ruwan D. Nawarathna, “A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification”, Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-5, 2017.
- P. Chinmayi and L. Agilandeeswari, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 1-12, 2017.
- Ketan Machhale and Hari Babu Nandpuru, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 1-7, 2015.
- M.F.B. Othman, N.B. Abdullah and N.F.B. Kamal, “MRI Brain Classification using Support Vector Machine”, Proceedings of 4th International Conference on Modelling, Simulation and Applied Optimization, pp. 1-4, 2011.
- Comparative Analysis of Genetic Algorithm - Support Vector Machine and Deep Learning with Convolutional Neural Network for Brain Tumor Detection and Classification
Abstract Views :147 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2159-2168Abstract
A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of high-quality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GA-SVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.Keywords
Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Genetic Algorithm Support Vector Machine, Convolutional Neural Network.References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, Alexandria Engineering Journal, Vol. 57, No. 4, pp. 2387-2392, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 1-7, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No 3, pp. 155-164, 2017.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the Help of KSVM”, International Journal of Engineering and Technical Research, Vol. 7, No. 2, pp. 1-12, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, Proceedings of IEEE International Conference on Computing for Sustainable Global Development, pp. 132-138, 2016.
- D. Haritha, “Brain Tumour Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Matthew, A. Prasad and P.B. Anto, “A Review on Feature Extraction Techniques for Tumour Detection and Classification from Brain MRI”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1766-1771, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour-based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- D.R. Byreddy and M. Raghunadh, “An Application of Geometric Active Contour in Bio-Medical Engineering”, Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, pp. 322-326, 2014.
- Ruchi D. Deshmukh and Prof. Chaya Jadhav, “Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology, Vol. 4, No. 4, pp. 133-136, 2014.
- A.R. Kavitha, L. Chitra and R. Kanaga, “Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 3, pp. 1468-1471, 2016.
- R.J. Ramteke and Y. Khachane Monali, “Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbor”, International Journal of Advanced Computer Research, Vol. 2, No. 4, pp. 190-196, 2012.
- Aurav Gupta and Vinay Singh, “Brain Tumor Segmentation and Classification using FCM and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.
- N. Vani, A. Sowmya and N. Jayamma, “Brain Tumor Classification using Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.
- S.U. Aswathy, G. Glan Devadhas and S.S. Kumar, “MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.
- Narmada M. Balasooriya and Ruwan D. Nawarathna, “A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification”, Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-12, 2017.
- P. Chinmayi and L. Agilandeeswari, M. Prabhukumar and K. Muralibabu, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 151-160, 2017.
- Ketan Machhale, Hari Babu Nandpuru, Vivek Kapur and Laxmi Kosta, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 1-9, 2015.
- M.F.B. Othman, N.B. Abdullah and N.F.B. Kamal, “MRI Brain Classification using Support Vector Machine”, Proceedings of 4th International Conference on Modeling, Simulation and Applied Optimization, pp. 1-4, 2011.
- V.K. Sachdeva, I. Gupta, N. Khandelwal and C.K. Ahuja, “Multiclass Brain Tumor Classification using GA-SVM”, Proceedings of International Conference on Developments in E-Systems Engineering, pp. 182-187, 2011.
- Ahmed Kharrat, Karim Gasmi , Mohamed Ben Messaoud, Nacera Benamrane and Abid Mohamed, “A Hybrid Approach for Automatic Classification of Brain MRI using Genetic Algorithm and Support Vector Machine”, Leonardo Journal of Sciences, Vol. 17, pp. 71-82, 2010.