A generalized spatial fuzzy C-means algorithm for medical image segmentation

Authors:
Huynh Van Lung;Jong-Myon Kim
Affiliations:
University of Ulsan, Ulsan, South Korea;School of Computer Engineering & Information Technology, University of Ulsan, Ulsan, South Korea
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 8
Cited 2

Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
An Adaptive Fuzzy Clustering Algorithm for Medical Image Segmentation

MIAR '01 Proceedings of the International Workshop on Medical Imaging and Augmented Reality (MIAR '01)
Improving fuzzy c-means clustering based on feature-weight learning

Pattern Recognition Letters
Modified fuzzy c-mean in medical image segmentation

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

IEEE Transactions on Information Technology in Biomedicine
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

Audio segmentation and classification using a temporally weighted fuzzy C-means algorithm

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
An enhanced fuzzy c-means algorithm for audio segmentation and classification

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation. To solve this problem, this paper proposes a robust segmentation technique, called a Generalized Spatial Fuzzy C-Means (GSFCM) algorithm, that utilizes both given pixel attributes and the spatial local information which is weighted correspondingly to neighbor elements based on their distance attributes. This improves the segmentation performance dramatically. Experimental results with several magnetic resonance (MR) images show that the proposed GSFCM algorithm outperforms the traditional FCM algorithms in the various cluster validity functions.