Commenced in January 2007
Paper Count: 30982
A 3D Approach for Extraction of the Coronaryartery and Quantification of the Stenosis
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057141Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1285
 X. Li, T. ZHANG, and Z. QU, "Image Segmentation Using Fuzzy Clustering with Spatial Constraints Based on Markov Random Field via Bayesian Theory", IEICE Trans. Fundamental, vol. E91-A, no.3, pp. 723-729, 2008.
 Y.A. Tolias and S.M. Panas, "On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system," IEEE Signal Process. Lett., vol.5, no.10, pp.245-247, 1998.
 D. L. Pham, "Spatial models for fuzzy clustering," Computer Vision and Image Understanding, vol.84, no.2, pp.285-297, 2001.
 S. Z. Li, "Markov Random Field Modeling in Computer Vision", Springer-Verlag, 1995.
 X. Ye, X. Lin, J. Dehmeshki, G. Slabaugh, and G. Beddoe, "Shape Based Computer Aided Detection of Lung Nodules in Thoracic CT Image," IEEE Trans. Biomedical Engineering, vol. 56, pp. 1810 - 1820, 2009.
 O. Demirkaya, M.Hakan Asyali, P. K. Sahoo, "Image Processing with MATLAB Applications in Medicine and Biology," CRC Press, 2009.
 N. Nicolaidis, I. Pitas, 3D image processing algorithm. London:wiley; 2001.
 K. Palagyi, A Kuba, "A 3D 6-subiteration thinning algorithm for extracting medial lines," Pattern Recogn Lett, vol. 19, pp. 613-627, 1998.
 C. Arcelli,G. S. D. Baja, "A width-independent fast thinning algorithm," IEEE Trans PAMI, vol. 7(4), pp. 463-474, 1985.
 P.J. Schneider, D.H. Eberly, Geometric tools for computer graphics, Morgan Kaufmann, 2003.
 H. Triebel, Interpolation theory, function spaces, differential operators, North-Holland Publishing Company , 1978.
 G. Soulez and S.D. Qanadli, "A multimodality vascular imaging phantom with fiducial markers visible in DSA, CTA, MRA, and ultrasound", Medical Physics, vol. 31, pp. 1424-1433, 2004.
 H. Scherl, J. Horngger, M. Prummer, M. Lell, "Semi-automatic level-set based segmentation and stenosis quantification of internal carotid artery in 3D CTA data sets," Medical Image Analysis, vol. 11, pp. 21-34, 2007.
 Y. Yang, A. Tannenbaum, D. Giddens, "Knowledge-Based 3D Segmentation and Reconstruction of Coronary Arteries Using CT Images", in Proc. 26th Annu. IEEE Conf. Engineering in Medicine and Biology Society, Atlanta, GA, USA, 2004.
 C. Metz, M. Schaap, A. Van Der Giessen, T. Van Walsum, W. Niessen, "semi-automatic coronary artery centerline exteraction in computed tomography angiography data," in Proc. 4th IEEE Int. Symp. Biomed. Imaging, Arlington, VA, pp. 856-85,2007.