A robust method of identifying and measuring fibrous cap in 3D time-of-flight MR image

A knowledge based system has been designed for identifying and measuring fibrous cap (FC) in magnetic resonance images (MRI). The proposed method consists of three basic procedures. First, we apply Markov random field (MRF) to segment the whole image into regions and automatically identify the inner boundary of FC based on prior knowledge of lumen area. An enhanced algorithm, called QHCF, is proposed to find the optimal region segmentation. In the second step, active contour model is employed in finding FC´s outer boundary. In this step, we use a new scheme which uses radial searching to find the end points so as to automatically initialize the contour tracking process. Finally, a thickness transformation is designed by extending binary mathematical morphology operation. Based on it, all the thickness related parameters can thus be measured quantitatively and consistently. Through these three integrated steps, FC is identified and measured. Experimental result demonstrates the encouraging performance of this algorithm.