Paper Info
Title
An Unsupervised Cluster-wise Color Segmentation of Medical and Camera Images using Genetically improved Fuzzy-Markovian Decision Relational Model.
Abstract
The traditional Fuzzy C-means (FCM) has been adopted worldwide to perform different kinds of image segmentation. However, owing to the fact that it is very susceptible to noise and other image artifacts, its usage is no longer a priority in the constantly changing real world application. The motivation of this paper is to propose a robust & unsupervised Image Segmentation framework known as GIFMRCM for enhancing the underlying delicate architectures of the human brain with ease. GIFMRCM introduces a new objective function by utilising a degree of mutual connectivity factor between pixels and the center. The manuscript can be broken up into two major constituents - Image Segmentation using GIFMRCM, and Cluster-wise color space representation of the GIFMRCM image using k-means hard clustering approach in a CIE L*a*b* color space. Experimentation on medical images shows that the proposed algorithm can improve the performance of image segmentation, and remove noise efficiently. The cluster-wise feature extraction procedure proposed in this paper is also able to extract delicate regions of human brain with ease.
Year
DOI
Venue
2018
10.3233/JIFS-17968
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Image segmentation,GIFMRCM,mutual connectivity,objective function,CIE L*a*b* color space
Markov process,Pattern recognition,Segmentation,Fuzzy logic,Artificial intelligence,Relational model,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
35
1
1064-1246
Citations 
PageRank 
References 
1
0.35
26
Authors
4
Name
Order
Citations
PageRank
Kishorjit Nongmeikapam1196.68
Wahengbam Kanan Kumar210.35
Ranita Khumukcham310.35
Aheibam Dinamani Singh421.38