Computer-aided detection of mesial temporal sclerosis based on hippocampus and cerebrospinal fluid features in MR images

• Autorzy: Wang Huiquan , Ahmed S. Nizam , Mandal Mrinal

Identyfikatory

DOI

10.1016/j.bbe.2018.10.005

• Języki publikacji: EN

Abstrakty

EN

Mesial temporal sclerosis (MTS) is the commonest brain abnormalities in patients with intractable epilepsy. Its diagnosis is usually performed by neuroradiologists based on visual inspection of magnetic resonance imaging (MRI) scans, which is a subjective and time-consuming process with inter-observer variability. In order to expedite the identification of MTS, an automated computer-aided method based on brain MRI characteristics is proposed in this paper. It includes brain segmentation and hippocampus extraction followed by calculating features of both hippocampus and its surrounding cerebrospinal fluid. After that, support vector machines are applied to the generated features to identify patients with MTS from those without MTS. The proposed technique is developed and evaluated on a data set comprising 15 normal controls, 18 left and 18 right MTS patients. Experimental results show that subjects are correctly classified using the proposed classifiers with an accuracy of 0.94 for both left and right MTS detection. Overall, the proposed method could identify MTS in brain MR images and show a promising performance, thus showing its potential clinical utility.

Słowa kluczowe

EN

mesial temporal sclerosis; hippocampus image; brain segmentation; support vector machine

PL

stwardnienie skroniowe; hipokamp; segmentacja mózgu; maszyna wektorów wsparcia

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 1
• Strony: 122--132
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Wang Huiquan

• Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

autor

Ahmed S. Nizam

• Department of Medicine, University of Alberta, Edmonton, Alberta, Canada

autor

Mandal Mrinal

• Department of Electrical and Computer Engineering, 11-203 Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 1H9

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).