Automatic detection of luminal contour in intravascular ultrasound images using fuzzy clustering and snakes

Abstract

Extraction of the luminal contours from the intravascular ultrasound (IVUS) images is very important to analysis and diagnosis of coronary heart disease. Manual processing of large IVUS data is quite tedious and time consuming. This paper presented an algorithm for automatic detection of the luminal contours in intravascular ultrasound images, based on fuzzy clustering and snakes. To solve the difficulty of automatic contour initialization, this paper used fuzzy clustering and spline interpolation to obtain the initial contour. First, fuzzy clustering was used to detect the luminal contours on the multiple longitudinal images. Then, luminal contour points were transformed into the individual transversal images. Those luminal contour points were spline-interpolated on these transversal images. The spline-interpolated contour was used as the initial contour of snakes. We evaluated automatically detection method based on the average contours obtained from expert manual segmentation as the ground truth, and the results had demonstrated that our method was accurate and efficient.