Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing

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題名:	Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing
作者:	王文俊;Lin, Geng-Cheng;Wang, Wen-June;Kang, Chung-Chia;Wang, Chuin-Mu
貢獻者:	資訊電機學院電機工程學系
關鍵詞:	Algorithms;Brain - anatomy & histology;Classification;Fuzzy Logic;Humans;Image Enhancement - methods;Image Interpretation, Computer-Assisted - methods;Magnetic resonance imaging (MRI);Magnetic Resonance Imaging - instrumentation;Magnetic Resonance Imaging - methods;Multispectral;Pattern Recognition, Automated - methods;Phantoms, Imaging;Radiology;Reproducibility of Results;Seeded region growing (SRG);Segmentation;Sensitivity and Specificity
日期:	2012-02-01
上傳時間:	2026-04-23 14:27:09 (UTC+8)
出版者:	Elsevier Inc.;Netherlands: Elsevier Inc
摘要:	摘要： Magnetic resonance imaging (MRI) is a valuable diagnostic tool in medical science due to its capability for soft-tissue characterization and three-dimensional visualization. One potential application of MRI in clinical practice is brain parenchyma classification and segmentation. Based on fuzzy knowledge and modified seeded region growing, this work proposes a novel image segmentation method, called Fuzzy Knowledge-Based Seeded Region Growing (FKSRG), for multispectral MR images. In this work, fuzzy knowledge includes the fuzzy edge, fuzzy similarity and fuzzy distance, which are obtained from relationships between pixels in multispectral MR images and are applied to the modified seeded regions growing process. In conventional regions merging, the final number of regions is unknown. Therefore, a Target Generation Process is proposed and applied to support conventional regions merging, such that the FKSRG method does not over- or undersegment images. Finally, two image sets, namely, computer-generated phantom images and real MR images, are used in experiments to assess the effectiveness of the proposed FKSRG method. Experimental results demonstrate that the FKSRG method segments multispectral MR images much more effectively than the Functional MRI of the Brain Automated Segmentation Tool, K-means and Support Vector Machine methods. 其他題名： Magn Reson Imaging 出版者： Netherlands: Elsevier Inc 出版日期： 2012-02-01 出處： Magnetic resonance imaging, 2012-02, Vol.30 (2), p.230-246 版權： 2012 版權： Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved. 識別號： ISSN: 0730-725X 識別號： ISSN: 1873-5894 識別號： EISSN: 1873-5894 識別號： DOI: 10.1016/j.mri.2011.09.008 識別號： PMID: 22133286
顯示於類別:	[電機工程學系] 期刊論文

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