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

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.