Abstract: Segmentation and quantification of stenosis is an
important task in assessing coronary artery disease. One of the main
challenges is measuring the real diameter of curved vessels.
Moreover, uncertainty in segmentation of different tissues in the
narrow vessel is an important issue that affects accuracy. This paper
proposes an algorithm to extract coronary arteries and measure the
degree of stenosis. Markovian fuzzy clustering method is applied to
model uncertainty arises from partial volume effect problem. The
algorithm employs: segmentation, centreline extraction, estimation of
orthogonal plane to centreline, measurement of the degree of
stenosis. To evaluate the accuracy and reproducibility, the approach
has been applied to a vascular phantom and the results are compared
with real diameter. The results of 10 patient datasets have been
visually judged by a qualified radiologist. The results reveal the
superiority of the proposed method compared to the Conventional
thresholding Method (CTM) on both datasets.