Automated Segmentation And Classification Technique For Brain Stroke

Mohd Saad, Norhashimah and Abdullah, Abdul Rahim and Mohd Noor, Niza Suzaini and Mohd Ali, Nursabillilah (2019) Automated Segmentation And Classification Technique For Brain Stroke. International Journal Of Electrical And Computer Engineering (IJECE), 9 (3). pp. 1832-1841. ISSN 2088-8708

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Abstract

Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images

Item Type:	Article
Uncontrolled Keywords:	Brain Imaging, Classification, Diffusion-Weighted Imaging, (DWI) Segmentation, Stroke
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	09 Dec 2020 11:47
Last Modified:	09 Dec 2020 11:47
URI:	http://eprints.utem.edu.my/id/eprint/24589
Statistic Details:	View Download Statistic

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