Rotational effect on ROI's for accurate lumen quantification in bifurcated MR plaque volumes

This paper presents a use of geometric-based method integrated with classifier for lumen wall estimation using MR plaque volumes. The following are the new things the readers will observe when it comes to plaque imaging, (a) Application of three different sets of classifiers (Fuzzy, Markovian and Graph-based) for lumen region classification in plaque MR volumes. These classifiers are used in multi-resolution framework, (b) Usage of rule-based region merging applied to the sub-classes of lumen region, (c) Rotational effect on region of interest in arterial bifurcation zones for accurate lumen region identification and boundary estimation. We have used our diagnostic system with three different classifying methods on actual patient data. We measure performance of the system by computing the mean distance error with respect to boundaries traced manually by human experts. Overall, the system consists of 22,500 boundary points. The in-plane pixel resolution is 0.25 millimeters. Using Markovian classifier method, the average error was 0.61 pixels; using Fuzzy classifier method, the average error was 0.62 pixels; using Graph-based classifier method, the average error was 0.74 pixels. All these methods lead to error less than 0.185 mm. We also validated our system by simulating the lumen images with additive Gaussian perturbations. This system works on a Linux platform and is written in C++.