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Brain tumor data collection and analysis for developing tumor growth model

Hameed, Shihab A. (2013) Brain tumor data collection and analysis for developing tumor growth model. Research Report. s.n, Kuala Lumpur. (Unpublished)

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This project, we present a novel, fast, hybrid and bi-level segmentation technique uniquely developed for segmentation of medical images. Medical images are generally characterized by multiple regions, and weak edges. When regions in medical images are viewed as made up of homogeneous group of intensities, it becomes more difficult to analyze because quite often different organs or anatomical structures may have similar gray level or intensity representation. The complexity of medical imagery is well catered for in this technique by starting-out with multiple thresholding, applying similarity segmentation method, and resolving boundary problem with template matching technique, and then a region of interest (ROI)segmentation that involves finding the edges of the object of interest (OOI)at final stage. This technique can also be adapted to segmentation of non-medical images

Item Type: Monograph (Research Report)
Additional Information: 4428/38690
Uncontrolled Keywords: Hybrid segmentation, multi level segmentation, medical images, template matching
Subjects: R Medicine > R Medicine (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Sr. Norsyaziela Zulkefli
Date Deposited: 14 May 2015 15:42
Last Modified: 14 May 2015 15:42
URI: http://irep.iium.edu.my/id/eprint/38690

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