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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| Format: | Monograph |
| Language: | English |
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s.n
2013
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| 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 |
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iium-38690 |
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eprints |
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iium-386902015-05-14T07:42:41Z http://irep.iium.edu.my/38690/ Brain tumor data collection and analysis for developing tumor growth model Hameed, Shihab A. R Medicine (General) 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 s.n 2013-07-08 Monograph NonPeerReviewed application/pdf en http://irep.iium.edu.my/38690/1/EDW_A12-626-1417.pdf Hameed, Shihab A. (2013) Brain tumor data collection and analysis for developing tumor growth model. Research Report. s.n, Kuala Lumpur. (Unpublished) EDW A12-626-1417 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
R Medicine (General) |
| spellingShingle |
R Medicine (General) Hameed, Shihab A. Brain tumor data collection and analysis for developing tumor growth model |
| description |
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 |
| format |
Monograph |
| author |
Hameed, Shihab A. |
| author_facet |
Hameed, Shihab A. |
| author_sort |
Hameed, Shihab A. |
| title |
Brain tumor data collection and analysis for developing tumor growth model |
| title_short |
Brain tumor data collection and analysis for developing tumor growth model |
| title_full |
Brain tumor data collection and analysis for developing tumor growth model |
| title_fullStr |
Brain tumor data collection and analysis for developing tumor growth model |
| title_full_unstemmed |
Brain tumor data collection and analysis for developing tumor growth model |
| title_sort |
brain tumor data collection and analysis for developing tumor growth model |
| publisher |
s.n |
| publishDate |
2013 |
| url |
http://irep.iium.edu.my/38690/ http://irep.iium.edu.my/38690/ http://irep.iium.edu.my/38690/1/EDW_A12-626-1417.pdf |
| first_indexed |
2023-09-18T20:55:36Z |
| last_indexed |
2023-09-18T20:55:36Z |
| _version_ |
1777410282960715776 |