A new 3D Markovian fuzzy approach for increasing the accuracy of measurement of the calcium deposit in 3D CTA images of the coronary artery

Accurate quantification of calcium deposit is an important task in assessing coronary artery disease, especially when the investigation of the disease progress is considered. The reproducibility and robustness of the segmentation algorithm against partial volume effect and noise is critical for an accurate quantification. While there are several approaches for segmentation of the volume of the blood vessel and hard plaque in the literature, the main drawback of these approaches is in making a deterministic decision in terms of assigning a particular voxel to only one type of tissue (such as blood vessel, hard plaque or surrounding area). However in reality, because of the partial volume effect, a voxel may contain more than one tissue type. In particular, using deterministic methods for quantification of the small objects such as thin blood vessels or hard plaque may lead to inaccurate results and higher inter and intra-scan variability. In this paper, we tackle the problem of the partial volume effect using a 3D adaptive fuzzy algorithm incorporating a Markov random field model. The method is shown to more accurately segment the blood vessel, hard plaque and surrounding tissue areas. The algorithm is applied to several datasets and the outcomes have been judged visually by a qualified radiologist. The proposed algorithm has the potential to be applied for the accurate quantification of the degree of stenosis.