Brain tumor data collection and analysis for developing tumor growth model

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 inten...

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Bibliographic Details
Main Author: Hameed, Shihab A.
Format: Monograph
Language:English
Published: s.n 2013
Subjects:
Online Access:http://irep.iium.edu.my/38690/
http://irep.iium.edu.my/38690/
http://irep.iium.edu.my/38690/1/EDW_A12-626-1417.pdf
Description
Summary: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