ICTACT Journal on Image and Video Processing

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Brain Tumour Segmentation Strategies Utilizing Mean Shift Clustering and Content based Active Contour Segmentation


Affiliations
1 Department of Electrical Engineering, PSG College of Technology, India
     

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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).
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  • Brain Tumour Segmentation Strategies Utilizing Mean Shift Clustering and Content based Active Contour Segmentation

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Authors

D. M. Mahalakshmi
Department of Electrical Engineering, PSG College of Technology, India
S. Sumathi
Department of Electrical Engineering, PSG College of Technology, India

Abstract


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