Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory

Segmentasi imej Magnetic Resonance (MR) merupakan satu tugas klinikal yang mencabar. Selalunya, satu jenis imej MR tidak mencukupi untuk memberikan maklumat yang lengkap mengenai sesuatu tisu patologi atau objek visual dari imej Accurate Magnetic Resonance (MR) image segmentation is a clinically...

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Main Author: Lim, Khai Yin
Format: Thesis
Language:English
Published: 2017
Subjects:
Online Access:http://eprints.usm.my/38873/
http://eprints.usm.my/38873/1/Segmentation_of_multisequence_medical_images_using_random_walks_algorithm_and_rough_sets_theory_by_Lim_Khai_Yin..pdf
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author Lim, Khai Yin
author_facet Lim, Khai Yin
author_sort Lim, Khai Yin
building USM Institutional Repository
collection Online Access
description Segmentasi imej Magnetic Resonance (MR) merupakan satu tugas klinikal yang mencabar. Selalunya, satu jenis imej MR tidak mencukupi untuk memberikan maklumat yang lengkap mengenai sesuatu tisu patologi atau objek visual dari imej Accurate Magnetic Resonance (MR) image segmentation is a clinically challenging task. More often than not, one type of MRI image is insufficient to provide the complete information about a pathological tissue or a visual object from the image
first_indexed 2025-11-15T17:33:44Z
format Thesis
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institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T17:33:44Z
publishDate 2017
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spelling usm-388732019-04-12T05:24:59Z http://eprints.usm.my/38873/ Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory Lim, Khai Yin QA75.5-76.95 Electronic computers. Computer science Segmentasi imej Magnetic Resonance (MR) merupakan satu tugas klinikal yang mencabar. Selalunya, satu jenis imej MR tidak mencukupi untuk memberikan maklumat yang lengkap mengenai sesuatu tisu patologi atau objek visual dari imej Accurate Magnetic Resonance (MR) image segmentation is a clinically challenging task. More often than not, one type of MRI image is insufficient to provide the complete information about a pathological tissue or a visual object from the image 2017-07 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/38873/1/Segmentation_of_multisequence_medical_images_using_random_walks_algorithm_and_rough_sets_theory_by_Lim_Khai_Yin..pdf Lim, Khai Yin (2017) Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA75.5-76.95 Electronic computers. Computer science
Lim, Khai Yin
Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title_full Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title_fullStr Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title_full_unstemmed Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title_short Segmentation Of Ultisequence Medical Images Using Random Walks Algorithm And Rough Sets Theory
title_sort segmentation of ultisequence medical images using random walks algorithm and rough sets theory
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/38873/
http://eprints.usm.my/38873/1/Segmentation_of_multisequence_medical_images_using_random_walks_algorithm_and_rough_sets_theory_by_Lim_Khai_Yin..pdf