A Set-based Hybrid Approach (SHA) for MRI segmentation

A Set-based Hybrid Approach (SHA) for MRI segmentation

This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using...

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Main Authors: Liu Jiang, Tze-Yun LEONG, Chee, Kin Ban, Tan, Boon Pin, Shuter, B., Wang, Shih-chang
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
Data compression
Fuzzy neural nets
Image coding
Image fusion
Packet radio networks
Telecommunication computing
Telecommunication congestion control
Wavelet transforms
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/2992
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-39922016-02-05T06:30:05Z A Set-based Hybrid Approach (SHA) for MRI segmentation Liu Jiang, Tze-Yun LEONG, Chee, Kin Ban Tan, Boon Pin Shuter, B. Wang, Shih-chang This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter. © 2006 IEEE. 2006-12-08T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/2992 info:doi/10.1109/ICARCV.2006.345358 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data compression Fuzzy neural nets Image coding Image fusion Packet radio networks Telecommunication computing Telecommunication congestion control Wavelet transforms Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data compression
Fuzzy neural nets
Image coding
Image fusion
Packet radio networks
Telecommunication computing
Telecommunication congestion control
Wavelet transforms
Numerical Analysis and Scientific Computing
spellingShingle Data compression
Fuzzy neural nets
Image coding
Image fusion
Packet radio networks
Telecommunication computing
Telecommunication congestion control
Wavelet transforms
Numerical Analysis and Scientific Computing
Liu Jiang,
Tze-Yun LEONG,
Chee, Kin Ban
Tan, Boon Pin
Shuter, B.
Wang, Shih-chang
A Set-based Hybrid Approach (SHA) for MRI segmentation
description This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter. © 2006 IEEE.
format text
author Liu Jiang,
Tze-Yun LEONG,
Chee, Kin Ban
Tan, Boon Pin
Shuter, B.
Wang, Shih-chang
author_facet Liu Jiang,
Tze-Yun LEONG,
Chee, Kin Ban
Tan, Boon Pin
Shuter, B.
Wang, Shih-chang
author_sort Liu Jiang,
title A Set-based Hybrid Approach (SHA) for MRI segmentation
title_short A Set-based Hybrid Approach (SHA) for MRI segmentation
title_full A Set-based Hybrid Approach (SHA) for MRI segmentation
title_fullStr A Set-based Hybrid Approach (SHA) for MRI segmentation
title_full_unstemmed A Set-based Hybrid Approach (SHA) for MRI segmentation
title_sort set-based hybrid approach (sha) for mri segmentation
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/2992
_version_ 1770572772384178176

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