Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

Abstract

Abstract. Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. In this paper, we present a method for the segmentation of the luminal border using IVUS radio frequency (RF) data. Specifically, we parameterize the lumen contour using Fourier series. This contour is deformed by minimizing a cost function that is formulated using a probabilistic approach in which the a priori term is obtained using the prediction confidence of a Support Vector Machine classifier using features extracted from the RF signal. We evaluated the performance of our method by comparing our results with manual segmentations from two expert observers on 280 frames from eight 40 MHz IVUS sequences from rabbits and pigs. The performance was evaluated using the Dice similarity coefficient, coefficient of determination, and linear regressions of the lumen area for each frame. Our results indicate the feasibility of our method for the segmentation of the lumen from IVUS RF data.

Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

“…In this method, the likelihood of each pixel to belong to lumen are computed using samples of the regions of interest on a number of frames of the sequence to be segmented. This method is capable of segmenting the lumen employing either the B-mode reconstruction images or the radio frequency (RF) IVUS data [27]. Ciompi et al [28] presented a method in which segmentation was tackled as a classification problem and solved using an error correcting output code technique.…”