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Case-control comparison brain lesion segmentation for early infarct detection
journal contribution
posted on 2018-11-01, 00:00 authored by Fung Fung Ting, Kok Swee Sim, Chee Peng LimChee Peng LimComputed 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.
History
Journal
Computerized medical imaging and graphicsVolume
69Pagination
82 - 95Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
eISSN
1879-0771Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2018, Elsevier Ltd.Usage metrics
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No categories selectedKeywords
Biomedical engineeringBrain lesionComputerized support of stroke diagnosisMedical imaging processingStrokeScience & TechnologyTechnologyLife Sciences & BiomedicineEngineering, BiomedicalRadiology, Nuclear Medicine & Medical ImagingEngineeringAUTOMATED DELINEATIONIMAGENEUROPSYCHOLOGYTOMOGRAPHYALGORITHMCT
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