Open AccessREVIEW ON AUTOMATIC PROCESSING OF BRAIN IMAGES FOR SEGMENTATION AND ABNORMALITY DETECTION
Publication year - 2021
Publication title -
european journal of molecular and clinical medicine
Language(s) - English
Resource type - Journals
ISSN - 2515-8260
DOI - 10.31838/ejmcm.07.11.27
Subject(s) - segmentation , artificial intelligence , computer science , abnormality , ignorance , image segmentation , process (computing) , pattern recognition (psychology) , computer vision , medicine , philosophy , epistemology , psychiatry , operating system
Brain tumour detection is very popular in the area of medical image processing. This isdue to the sensitivity of brain functionality and inter structure. Any kind of ignorancetowards the problems related with brain may cause serious impact on human life/life style.Therefore, early detection or diagnosis of abnormalities or tumours helps the doctors andpatients to rectify the brain related health problems. The images are obtained throughscanning techniques which are very common. Images obtained from the scanning needs tobe segmented carefully for the future analysis and damage control procedures. In thispaper, a detailed review on different types of segmentation techniques proposed by variousauthors is studied and compared for a clear understanding of existing segmentationtechniques. They are tabulated to summarize different methodologies, segmentationtechniques, and existing processes for further studies on Brain image segmentation.Finally, a brief understanding towards deep learning techniques is studied in this paper tounderstand their role in modern era for automated segmentation process