A hybrid approach for the delineation of brain lesion from CT images

• Autorzy: Gautam A. , Raman B. , Raghuvanshi S.

Identyfikatory

DOI

10.1016/j.bbe.2018.04.003

• Języki publikacji: EN

Abstrakty

EN

Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.

Słowa kluczowe

EN

brain lesion segmentation; fuzzy c-means; kernel function; level set; distance regularized level set evolution

PL

segmentacja uszkodzeń mózgu; grupowanie rozmyte; funkcja jądra; metoda ustalania poziomu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 3
• Strony: 504--518
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Gautam A.

• Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

autor

Raman B.

• Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

autor

Raghuvanshi S.

• Department of Radiology, Himalayan Institute of Medical Sciences, Jolly Grant, Dehradun, India

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Uwagi

PL

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