Open AccessImplementation and quality measures of graph theory model based image segmentation process in medical application
Author(s) -
U Sekar,
Renjith Mohan,
MV Subba Reddy
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1921/1/012128
Subject(s) - cluster analysis , segmentation , artificial intelligence , computer science , graph theory , image segmentation , graph , representation (politics) , image quality , process (computing) , medical imaging , pattern recognition (psychology) , computer vision , quality (philosophy) , image (mathematics) , data mining , mathematics , theoretical computer science , combinatorics , politics , political science , law , operating system , philosophy , epistemology
This research work simplified the representation of an image into more significant and easier way to analyse the image segmentation process by applying graph theory using color spatial clustering with consensus region merging. The color spatial clustering with consensus region merging is compared with other traditional and graph theory model to analyse the various quality measures calculated for the input of magnetic resonance imaging (MRI) scan and X-Ray images which will be useful in medical imaging for better analysis during diagnosis. From quality measures, the proposed method shows good quality image parameters as it has lower MSE, NAE values.