A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation

Sayan Kahali; Jamuna Kanta Sing; Punam Kumar Saha

doi:10.1007/s00500-018-3594-y

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A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation

Journal article

Peer reviewed

A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation

Sayan Kahali, Jamuna Kanta Sing and Punam Kumar Saha

Soft computing (Berlin, Germany), Vol.23(20), pp.10407-10414

10/25/2018

DOI: 10.1007/s00500-018-3594-y

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Abstract

Automated segmentation of different tissue regions from brain magnetic resonance (MR) imaging has a substantial impact on many computer-assisted neuro-imaging studies. Major challenges to accomplish this task emerge from limited spatial resolution, signal-to-noise ratio, and RF coil inhomogeneity. These imaging artifacts lead to fuzziness of tissue boundaries and uncertainty in MR intensity-based tissue characterization at individual image voxels. The conventional fuzzy c -means (FCM) algorithm fails to produce satisfactory results for noisy image. In this paper, we present an entropy-based FCM segmentation method that incorporates the uncertainty of classification of individual pixels within the classical framework of FCM. Furthermore, instead of Euclidean distance, we have defined the non-Euclidean distance based on Gaussian probability density function. The new segmentation method was applied to Brainweb brain MR database at varying noise and inhomogeneity, and its performance was compared with existing FCM-based algorithms. The proposed method yields superior performance over some classical state-of-the-art methods. In addition to this, we also have performed the proposed method on some in vivo human brain MR data to demonstrate its performance.

Artificial Intelligence

Computational Intelligence

Control

Engineering

Mathematical Logic and Foundations

Mechatronics

Methodologies and Application

Robotics

Details

Title: Subtitle: A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation
Creators: Sayan Kahali - Siliguri Institute of Technology
Jamuna Kanta Sing - Jadavpur University
Punam Kumar Saha - University of Iowa
Resource Type: Journal article
Publication Details: Soft computing (Berlin, Germany), Vol.23(20), pp.10407-10414
Publisher: Springer Berlin Heidelberg
DOI: 10.1007/s00500-018-3594-y
ISSN: 1432-7643
eISSN: 1433-7479
Language: English
Date published: 10/25/2018
Academic Unit: Electrical and Computer Engineering; Radiology
Record Identifier: 9984197067502771

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