NDLI: Role of fuzzy pre-classifier for high performance LI/MA segmentation in B-mode longitudinal carotid ultrasound images

Content Provider	IEEE Xplore Digital Library
Author	Molinari, F. Gaetano, L. Balestra, G. Suri, J.S.
Copyright Year	2010
Description	Author affiliation: BioLab, Department of Electronics, Politecnico di Torino, Italy (Molinari, F.; Balestra, G.) \|\| Department of Mechanics, Politecnico di Torino, Italy (Gaetano, L.) \|\| Biomedical Technologies Inc., Denver, CO, USA (Suri, J.S.)
Abstract	The automated segmentation of the carotid artery wall from ultrasound images is required for an accurate measurement of the artery intima-media thickness. Segmentation accuracy of automated techniques is usually limited by noise and artifacts. In 2005, the authors developed a methodology called CULEX whose performance was noise sensitive. The final stage of CULEX segmentation was fuzzy clustering of the pixels, to detect the lumen-intima (LI) and media-adventitia (MA) carotid wall interfaces. In this paper, we show the effect of a fuzzy Mamdani-type pre-classifier used to improve the segmentation performance. Thanks to the Mamdami fuzzy pre-classifier, we optimized the de-fuzzyfication threshold, increasing the LI and MA performance by 62% and 3.5%, respectively. The obtained segmentation errors (55.6±69.4 µm for LI and 34.4±24.4 µm for MA), validated against human tracings and on a 200-images dataset containing a mixture of healthy and plaque vessels.
Starting Page	4719
Ending Page	4722
File Size	1462817
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626390
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Image segmentation Ultrasonic imaging Backscatter Blood Noise Arteries
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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