The well being of any country or society depends upon the healthy state of its population. One typical health issue faced by the developed, developing and underdeveloped societies is malignancy. The important challenge in image processing is the presence of noise during the image capture process which may result either under segmentation or over segmentation .Image segmentation is considered as the most vital and preliminary process in image analysis for Computer Aided Diagnosis. Segmentation of an image is the separation or division of the image in to different regions of similar feature. In brain MRI analysis, image segmentation is used for segmenting the brain tumor for the measurement of tumor area and for surgical planning. In medical image segmentation if any segmentation fails to detect the Region of Interest precisely, it leads to misclassification which will have serious affect on human beings. The work focuses on these challenges and has implemented effective methods for pre-processing and segmentation for processing MRI images. The exploratory results illuminate the effectiveness in dealing with higher number of segmentation issues by means of enhancing the segmentation quality and precision. The performance measures probability random index (PRI), global consistency error (GCE), structural similarity (SSIM), variation of information (VOI), Peak signal to noise ratio(PSNR) integrated in GUI-CAD is explained.
Dr.Karnam GOPI recevied his Masters degree in Applied Electronics in the year 2007 and Ph.D degree in Electronics and communication from S.V.University, Tirupati in 2018 respectively. He is working as Associate Professor in Sreenivasa Institute of Technology and Management studies, Chittoor.He has 15 years of teaching experience and published 15 papers in digital image processing.
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LAP LAMBERT Academic Publishing
segmentation, enhancement, Denoising, Computer Aided Diagnosis
TECHNOLOGY / Electronics / General