Case-control comparison brain lesion segmentation for early infarct detection

Citation

Ting, Fung Fung and Sim, Kok Swee and Lim, Chee Peng (2018) Case-control comparison brain lesion segmentation for early infarct detection. Computerized Medical Imaging and Graphics, 69. pp. 82-95. ISSN 0895-6111

Full text not available from this repository.

Abstract

Computed Tomography (CT) images are widely used for the identification of abnormal brain tissues following infarct and hemorrhage of a stroke. The treatment of this medical condition mainly depends on doctors’ experience. While manual lesion delineation by medical doctors is currently considered as the standard approach, it is time-consuming and dependent on each doctor’s expertise and experience. In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed to segment the region pertaining to brain injury by comparing the voxel intensity of CT images between control subjects and stroke patients. The method is able to segment the brain lesion from the stacked CT images automatically without prior knowledge of the location or the presence of the lesion. The aim is to reduce medical doctors' burden and assist them in making an accurate diagnosis. A case study with 300 sets of CT images from control subjects and stroke patients is conducted. Comparing with other existing methods, the outcome ascertains the effectiveness of the proposed method in detecting brain infarct of stroke patients.

Item Type: Article
Uncontrolled Keywords: Imaging systems in medicine, Medical imaging processing, Brain lesion, Stroke, Biomedical engineering, Computerized support of stroke diagnosis
Subjects: R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 08 Nov 2020 15:53
Last Modified: 08 Nov 2020 15:53
URII: http://shdl.mmu.edu.my/id/eprint/7263

Downloads

Downloads per month over past year

View ItemEdit (login required)