Title: Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model

Authors: Nagaraja Perumal; Kalaiselvi Thiruvenkadam

Addresses: Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education (Deemed to be University), Krishnankoil, Tamil Nadu, India ' Department of Computer Science and Applications, The Gandhigram Rural Institute, Gandhigram, Tamil Nadu, India

Abstract: The proposed method is based on multimodal brain tumour segmentation method (MBTSM) using improved fuzzy c-means (IFCM) and active contour model (ACM). This proposed MBTSM presents a brain tissue and tumour segmentation method that segments magnetic resonance imaging (MRI) of human head scans into grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), oedema, core tumour and compete tumour. The proposed method consists of three stages. Stage 1 is an IFCM method, modifying the conventional FCM for brain tissue segmentation process and this method gives comparable results than existing segmentation techniques. Stage 2 is an abnormal detection process that helps to check the results of IFCM method by fuzzy symmetric measure (FSM). Stage 3 is segment the tumour region from multimodal MRI head scans by modified Chan-Vese (MCV) model. The accuracy analysis of proposed MBTSM used the parameters dice coefficient (DC), positive predictive value (PPV), sensitivity, kappa coefficient (KC) and processing time. The mean DC values are 83% for GM, 86% for WM, 13% for CSF and 75% for complete tumour.

Keywords: brain tumour; clustering; magnetic resonance image; segmentation; active contour.

DOI: 10.1504/IJBET.2022.124018

International Journal of Biomedical Engineering and Technology, 2022 Vol.39 No.2, pp.188 - 211

Received: 20 Feb 2019
Accepted: 03 May 2019
Published online: 11 Jul 2022 *

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