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International Journal of Biomedical Imaging
Volume 2007 (2007), Article ID 25182, 9 pages
http://dx.doi.org/10.1155/2007/25182
Research Article

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Department of Electronic Engineering, Fudan University, Shanghai 200433, China

Received 18 January 2007; Revised 28 April 2007; Accepted 17 July 2007

Academic Editor: Jie Tian

Copyright © 2007 Jinhua Yu and Yuanyuan Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96±0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm.