An efficient SOM and EM-based intravascular ultrasound blood vessel image segmentation approach

Intravascular ultrasound (IVUS) is a catheter-based imaging method used in the study of atherosclerotic disease. IVUS produces cross-sectional images of the blood vessels that enable quantitative assessment of the plaque. Automatic segmentation of the anatomical structures in the IVUS image is a really challenging task due to the presence of noise and catheter artifacts. Hence, this paper presents an efficient self-organizing map (SOM) and expectation-maximization (EM)-based approach for the segmentation of cross-sectional view of the IVUS blood vessel image. In our proposed work, the directional filtering is used to improve the signal to noise ratio of the blood vessel image. The Hough transform is used for predicting the circle in the image. Segmentation of the image is performed using the SOM and EM algorithm. After the segmentation process, extraction of the common pixels is performed. Gray-level co-occurrence matrix is applied for extracting features from the image. Fuzzy-relevance vector machine based classification of the image is performed. From the comparison results, it is clearly observed that the proposed approach is highly efficient than the existing techniques.