An innovative application of fuzzy clustering and mathematical morphology for the problem of luminal contour detection in intravascular ultrasound images is presented. Median and standard deviation are used as features for segmentation process. Comparison was made with gold standard segmented images obtained from the average of images segmented by experienced medical doctors. Tests were carried out with 20 in vivo coronary images obtained from different patients. High correlation coefficients were found between lumen regions manually and automatically defined when area, mean gray level, and standard deviation of the lumen regions were compared.
Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images. Publishing Authors By Initials
Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images. Journal Published:
PUBLICATION TYPE: Journal Article
Journal: Conference proceedings : ... Annual International
VOLUME: 4
Page Numbers: 3471-4
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ISSN: 1557-170X
DAY: 6
MONTH: 02
YEAR: 2005
Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images. Information
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LANGUAGE: eng
NlmUniqueID: 101243413
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Grant and Affiliation Information for Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images.
AFFILIATION: Graduate School of Engineering, Tohoku University, Aoba 6-6-05, Aoba-ku, Sendai 980-8579, Japan (esmeraldo@ieee.org).
Country: United States
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MEDLINETA: Conf Proc IEEE Eng Med Biol So
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