Seeded Region Growing Based on Extension for Multispectral MR Images Classification

Yen Che Chang; Kuei Ting Kuo; Zih Yi Wang; Chuin Mu Wang

doi:10.4028/www.scientific.net/AMR.1079-1080.872

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1079-1080Seeded Region Growing Based on Extension for...

Seeded Region Growing Based on Extension for Multispectral MR Images Classification

Abstract:

In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.

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Periodical:

Advanced Materials Research (Volumes 1079-1080)

Pages:

872-877

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1079-1080.872

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Online since:

December 2014

Authors:

Yen Che Chang, Kuei Ting Kuo, Zih Yi Wang*, Chuin Mu Wang

Keywords:

Extension, MRI, Multispectral Images, Seeded Region Growing

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