Citation

Teh, V. and Sim, Kok Swee and Wong, Eng Kiong (2016) Brain early infarct detection using gamma correction extreme-level eliminating with weighting distribution. Scanning, 38 (6). pp. 842-856. ISSN 0161-0457; eISSN: 1932-8745

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Abstract

According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module.

Item Type:	Article
Uncontrolled Keywords:	contrast enhancement; extreme-level eliminating; gamma correction; histogram equalization; weighting distribution
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	12 Dec 2017 16:31
Last Modified:	12 Dec 2017 16:31
URII:	http://shdl.mmu.edu.my/id/eprint/6616

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