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

Geng Cheng Lin, Wen June Wang, Chung Chia Kang, Chuin Mu Wang

研究成果: 雜誌貢獻期刊論文同行評審

72 引文 斯高帕斯(Scopus)

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.

原文???core.languages.en_GB???
頁(從 - 到)230-246
頁數17
期刊Magnetic Resonance Imaging
30
發行號2
DOIs
出版狀態已出版 - 2月 2012

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