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Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier
Author(s) -
Anbumozhi Selladurai
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22380
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , cluster analysis , feature extraction , adaptive neuro fuzzy inference system , histogram equalization , computer vision , histogram , fuzzy logic , fuzzy control system , image (mathematics)
A stroke or “brain attack” occurs when the blood flow to an area of the brain is interrupted. In this article, ischemic stroke is detected and diagnosed using the following stages: noise reduction, enhancement, skull removal, feature extraction and k‐means clustering. The impulse noises in brain magnetic resonance imaging (MRI) image are reduced using directional filtering algorithm. The noise reduced brain image is further enhanced using oriented local histogram equalization technique. The skull is removed from the enhanced brain image. Features are extracted and stroke region is segmented using k‐means clustering and adaptive neuro fuzzy inference system (ANFIS) classifier. The main objective of this article is to develop a methodology for the detection of stroke using MRI brain images.