Title: MRI brain segmentation using correlation based on adaptively regularised kernel-based fuzzy C-means clustering

Authors: Y. Ambica; N. Subhash Chandra

Addresses: Computer Science Engineering Department, Arkay College of Engineering and Technology, India ' Department of Computer Science and Engineering, CVR College of Engineering, Vastu Nagar, Mangalpalli, Ibrahimpatnam, Ranga Reddy, India

Abstract: Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.

Keywords: fuzzy C-means; magnetic resonance imaging; medical image segmentation; Otsu thresholding.

DOI: 10.1504/IJAIP.2021.115250

International Journal of Advanced Intelligence Paradigms, 2021 Vol.19 No.2, pp.216 - 232

Received: 02 May 2018
Accepted: 16 Nov 2018

Published online: 14 May 2021 *

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