Case-control comparison brain lesion segmentation for early infarct detection. (November 2018)

• Record Type: Journal Article
• Authors: Ting, Fung Fung; Sim, Kok Swee; Lim, Chee Peng
• Is Part Of: Computerized medical imaging and graphics. Volume 69(2018)
• Page Start: 82
• Page End: 95
• Publication Date: 2018-11
• Subjects: Medical imaging processing -- Brain lesion -- Stroke -- Biomedical engineering -- Computerized support of stroke diagnosis; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.08.011
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: In this study, a case-control comparison brain lesion segmentation (CCBLS) method is proposed. To segment the region of brain injury by comparing the voxel intensity of CT images. Statistical case-control method to detect and segment stroke lesion automatically without prior knowledge with respect to its presence. To ease medical doctors' burden and assist them in the diagnostic process. 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.