Universiti Teknologi Malaysia Institutional Repository

Brain lesion segmentation using fuzzy C-means on diffusion-weighted imaging

Muda, A. F. and Saad, N. M. and Abu Bakar, S. A. R. and Muda, S. and Abdullah, A. R. (2015) Brain lesion segmentation using fuzzy C-means on diffusion-weighted imaging. Arpn Jourl Of Engineering And Applied Sciences, 10 (3). pp. 1138-1144. ISSN 1819-6608

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Official URL: http://www.arpnjournals.com/jeas/research_papers/r...

Abstract

This paper presents an automatic segmentation of brain lesions from diffusion-weighted imaging (DWI) usingFuzzy C-Means (FCM) algorithm. The lesions are acute stroke, tumour and chronic stroke. Pre-processing is applied to theDWI for intensity normalization, background removal and enhancement. After that, FCM is used for the segmentationprocess. FCM is an iterative process, where the process will stop when the maximum number of iterations is reached or theiteration is repeated until a set point known as the threshold is reached. The FCM provides good segmentation result inhyperintensity and hypointensity lesions according to the high value of the area overlap, and low value of false positive andfalse negative rates. The average dice indices are 0.73(acute stroke), 0.68 (tumour) and 0.82 (chronic stroke).

Item Type:Article
Subjects:A General Works
ID Code:57977
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:24 Aug 2017 09:08

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