Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

Version 1 : Received: 21 April 2022 / Approved: 26 April 2022 / Online: 26 April 2022 (14:11:52 CEST)

How to cite: S. V., A.K.; Yaghoubi, E.; Proença, H. A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation. Preprints 2022, 2022040244 (doi: 10.20944/preprints202204.0244.v1). S. V., A.K.; Yaghoubi, E.; Proença, H. A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation. Preprints 2022, 2022040244 (doi: 10.20944/preprints202204.0244.v1).

Abstract

Brain tissue segmentation is an important component of clinical diagnosis of brain diseases by means of multi-modal magnetic resonance imaging (MR). Brain tissue segmentation is developed by many unsupervised methods in literature. The most commonly used unsupervised methods are: K-Means, Expectation Maximization and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images which are complex, largely uncertain and imprecise in nature. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulted from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulted from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the arts.

Keywords

Brain Tissue Segmentation; Consensus Clustering; Segmentation; Magnetic Resonance Image

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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