Brain Tissue Segmentation Using Conditional Spatial Gustafson-Kessel Clustering

Articles in Press

Document Type : Original Article

Authors

1 Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran.

2 Professor/Faculty of Biomedical Engineering, Sahand University of Technology

3 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

Abstract

The segmentation of brain tissues is a crucial step in evaluating morphological changes in various brain regions for the identification of different diseases. This process is influenced by factors such as noise and intensity non-uniformity. Fuzzy c-means clustering (FCM) is a widely used method for image segmentation but is sensitive to noise, and its convergence rate is affected by data distribution. FCM-based approaches typically use Euclidean distance for clustering, assuming a spherical distribution of data, which overlooks distance variations in similar and compact clusters. Additionally, varying levels of intensity non-uniformity can impact clustering performance. To address these limitations, this study presents the Conditional Spatial Gustafson-Kessel (CSGK) algorithm, which performs well in segmenting compact clusters such as cerebrospinal fluid (CSF) by considering data distribution in an elliptical space. The robustness of the conventional Gustafson-Kessel algorithm is enhanced by incorporating both local and global spatial information into a weighted membership function. Furthermore, a Wiener filter combined with wavelet transform (WFWT) is applied during preprocessing to reduce the sensitivity of input data to intensity non-uniformity. Experimental results demonstrate that CSGK is an accurate algorithm for segmenting multiple brain tissues across different levels of noise and intensity non-uniformity.

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Tabriz Journal of Electrical Engineering

Articles in Press, Corrected Proof
Available Online from 10 March 2025

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