Molinari Filippo, Gaetano Laura, Balestra Gabriella, Suri Jasjit S
BioLab, Department of Electronics, Politecnico di Torino, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4719-22. doi: 10.1109/IEMBS.2010.5626390.
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 microm for LI and 34.4 ± 24.4 microm for MA), validated against human tracings and on a 200-images dataset containing a mixture of healthy and plaque vessels.
为了准确测量动脉内膜中层厚度，需要从超声图像中自动分割颈动脉壁。自动技术的分割精度通常受噪声和伪影的限制。2005年，作者开发了一种名为CULEX的方法，其性能对噪声敏感。CULEX分割的最后阶段是对像素进行模糊聚类，以检测管腔内膜（LI）和中膜外膜（MA）颈动脉壁界面。在本文中，我们展示了用于提高分割性能的模糊Mamdani型预分类器的效果。由于Mamdami模糊预分类器，我们优化了去模糊化阈值，使LI和MA的性能分别提高了62%和3.5%。所获得的分割误差（LI为55.6±69.4微米，MA为34.4±24.4微米），是在一个包含健康血管和有斑块血管的200幅图像数据集上，对照人工追踪进行验证的。
