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Abstract

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