DocumentCode
3108620
Title
Medical image granulation by fuzzy inference
Author
Hata, Yutaka ; Hirano, Shoji ; Kamiura, Naotake
Author_Institution
Dept. of Comput. Eng., Himeji Inst. of Technol., Hyogo, Japan
fYear
1998
fDate
20-21 Aug 1998
Firstpage
188
Lastpage
192
Abstract
The paper proposes a scheme of image granulation using the fuzzy inference technique. For a region of interest (ROI) in a medical image, the authors describe knowledge needed to granulate the ROI, for example, knowledge of intensity, location and so on. Generally, one cannot granulate the ROI without employing the whole of the knowledge. Fuzzy inference rules of the derived knowledge can accommodate the granulation. After the inference results are compiled to a total degree, resultant data is obtained. Clustering or a region growing technique is used to granulate the ROI. The experimental results on human brain MR images and human foot CT images show that the method can precisely granulate the ROI
Keywords
fuzzy logic; image segmentation; inference mechanisms; medical image processing; clustering; fuzzy inference; human brain MR images; human foot CT images; knowledge; medical image granulation; region growing technique; region of interest; Biomedical engineering; Biomedical imaging; Computed tomography; Data visualization; Foot; Fuzzy logic; Gravity; Humans; Image segmentation; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location
Pensacola Beach, FL
Print_ISBN
0-7803-4453-7
Type
conf
DOI
10.1109/NAFIPS.1998.715562
Filename
715562
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