Paper Info
Title
Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation.
Abstract
Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.
Year
DOI
Venue
2013
10.1007/s00521-011-0792-2
Neural Computing and Applications
Keywords
DocType
Volume
Artificial intelligence, Brain images, Convergence rate, Fuzzy C-means, Segmentation
Journal
22
Issue
ISSN
Citations 
5
1433-3058
5
PageRank 
References 
Authors
0.44
11
4
Name
Order
Citations
PageRank
D. Jude Hemanth111922.74
C. Kezi Selva Vijila2214.29
A. Immanuel Selvakumar3354.31
J. Anitha410016.43