A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation

Authors:
Hyunh Luong;Jong Myon Kim
Affiliations:
School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, Korea 680-749;School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, Korea 680-749
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

Citing 4
Cited 0

A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Unsupervised Image Segmentation Using Automated Fuzzy c-Means

CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Implementing and evaluating color-aware instruction set for low-memory, embedded video processing in data parallel architectures

EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Medical image segmentation plays an important role in medical image analysis and visualization. The Fuzzy c-Means (FCM) is one of the well-known methods in the practical applications of medical image segmentation. FCM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FCM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FCM method and provides reliable and efficient processing on CT and MRI image segmentation.