Image segmentation using fuzzy c-means clustering techniques in CT-scan images

Suzelawati Zenian and Kam, Siau Pey (2024) Image segmentation using fuzzy c-means clustering techniques in CT-scan images.

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Abstract

This paper investigates the use of fuzzy c-means clustering techniques for image segmentation in CT-scan images. Accurate segmentation of medical images is crucial for clinical diagnosis, but the similar intensity of gray levels in CT-scans poses a significant challenge. To address this, we explore three distinct fuzzy c-mean clustering approaches: classical fuzzy c-mean, fuzzy c-mean with a cooperation center, and type-II fuzzy c-mean clustering. These methods are employed to determine thresholds within membership functions, enabling effective fuzzy segmentation. We evaluate the performance of each technique on a consistent CT-scan dataset, utilizing partition coefficient and dice similarity index as metrics. Our findings indicate that fuzzy c-mean clustering with a cooperation center outperforms both classical fuzzy c-mean and type-II fuzzy c-mean clustering in segmentation accuracy.

Item Type:	Proceedings
Keyword:	Image segmentation, fuzzy c-means, type-II fuzzy c-means, noise
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA1-43 General R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC71-78.7 Examination. Diagnosis Including radiography
Department:	FACULTY > Faculty of Science and Natural Resources
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	17 Mar 2025 11:07
Last Modified:	17 Mar 2025 11:07
URI:	https://eprints.ums.edu.my/id/eprint/43219

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