Rician Noise Removal and Straightening of Blood Vessel in MR Images
Keywords:Rician noise, Fast gradient projection, Extracting, Centerline, Straightening
Purpose: To improve the visualization of severities in blood vessels combined Smoothing and straightening of a blood vessel was attempted for effective diagnosis of tumors and platelet formation in blood vessels.
Method: Fast gradient projection method was used for removing the Rician noise present in MR images. The smoothed image was extracted using the binarization technique and the extracted blood vessels are straightened using a tangent function.
Result: These techniques were tested in a variety of MR images and the results of this study reveal that the proposed method removed the Rician noise without affecting the diagnostic details and compared with existing methods quantitatively, extracted and straightened the blood vessels for making a clear decision about the severity of the disease.
Conclusion: The smoothed, extracted and straightened images are very much useful for the diagnosis of tissue characterization and tumor detection.
(1) Donnell TO, Gupta A, Boult T. A new model for the recovery of cylindrical structures from medical image data. CVRMed-MRCAS 1997;1205:223-32.
(2) Barrett SD, Carvalho CRD. A software tool to straighten curved chromosome images. Chromosome Research Kluwer Academic Publishers 2003;11:83-8.
(3) Zhang S, Levin DC, Halpern EJ, Fischman D, Savage M, Walinsky P. Accuracy of MDCT in Assessing the Degree of Stenosis Caused by Calcified Coronary Artery Plaques. AJR Am J Roentgenol 2008;191(6):1676-83.
(4) Chuang CL, Chen CM. Accurately and Practical Image Segmentation Algorithm in Coronary Artery. IFMBE Proceedings;14:2483-7.
(5) Kanitsar A, Wegenkittl R, Fleischmann D, Groller ME. Advanced Curved Planar Reformation: Flattening of Vascular Structures. IEEE Transaction on Visualization 2003, VIS 2003;43-50.
(6) Kanitsar A, Fleischmann D, Wegenkittl R, Felkel P. CPR – curved planar reformation. IEEE Transaction on Visualization, USA 2002;37-44.
(7) Wang Y. Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography. School of Engineering & Mathematical Sciences Dissertations, City University London 2011.
(8) Wang C. Computer-Assisted Coronary CT Angiography Analysis Curved MPR. Linköping University Medical Dissertations, Sweden 2011;1237.
(9) Zhang DP. Coronary Artery Segmentation and Motion Modelling. Department of Computing Dissertations, Imperial College London 2010.
(10) Wink O, Hecht HS, Ruijters D. Coronary Computed Tomographic Angiography in the Cardiac Catheterization Laboratory: Current Applications and Future Developments. Advances in Coronary Angiography, Cardiology Clinics 2009;27(3):513-29.
(11) Saran A. N., Nar F., and Saran M., Vessel segmentation in MRI using a variational image subtraction approach, Turkish Journal of Electrical Engineering & Comp Sciences, 2014, 22: 499-516.
(12) Lawler LP. CT scanning of the coronary arteries: How to do it and how to interpret it. Appl Radiol 2005;34(10):8-16.
(13) Kanitsar AM. Curved Planar Reformation for Vessel Visualization. Institute for Computer Graphics and Algorithms Dissertation, Vienna University of Technology 2004.
(14) Kim Do-Yeon, 3D volume extraction of cerebrovascular structure on brain magnetic resonance angiography data sets, J. Biomedical Science and Engineering, 2012, 5: 574-9.
(15) Olabarriaga SD, Breeuwer M, Niessen WJ. Evaluation of Hessian-based filters to enhance the axis of coronary arteries in CT images. International Congress Series 2003;1256:1191-6.
(16) Marco Antonio Zenteno M A., Santos-Franco J A., Moscote-Salazar L R., Lee A., Endovascular remodeling of tortuous cervical segments of the internal carotid artery that hinder the management of complex intracranial aneurysms, Romanian Neurosurgery, 2013, XX(3):
(17) Dupej J., Blood Vessel Visualization on CT Data, WDS'12 Proceedings of Contributed Papers, 2012, Part I, 88-93.
(18) Ropinski T, Hermann S, Reich R, Schafers M, Hinrichs K. Multimodal Vessel Visualization of Mouse Aorta PET/CT Scans. IEEE Trans Vis Comput Graph 2009;15(6):1515-22.
(19) Hénault DR, Sundar H, Cheriet M. Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions. IEEE Transactions on Medical Imaging 2012;31(8):1557-72.
(20) Kocsis E, Trus BL, Steer CJ, Bisher ME, Steven AC. Straighten Curved Objects. J. Struct. Biol 1991;107:6-14.
(21) Wassermann D, Adad JC, Lehericy S, Benali H, Rossignol S, Deriche R, Straightening the Spinal Cord using Fiber Tractography. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2010;1377-80.
(22) Angelelli P, Hauser H. Straightening Tubular Flow for Side-by-Side Visualization. IEEE Transactions on Visualization and Computer Graphics 2011;17(12):2063-70.
(23) Mistelbauer G, Morar A, Varchola A, Schernthaner R, Baclija I, Kochl A, Kanitsar A, Bruckner S, Groller E. Vessel Visualization using Curvicircular Feature Aggregation Existing. Eurographics Conference on Visualization (EuroVis), Computer Graphics Forum 2013;32(3):231-40.
(24) Lee N, Rasch M. Tangential curved planar reformation for topological and orientation invariant visualization of vascular trees. Conf Proc on IEEE Engg Med Biol Soc 2006;1:1073-76.
(25) Goldstein AA. Convex programming in Hilbert space. Bull. Amer. Math. Soc 1964;70:709-10.
(26) Levitin ES, Polyak BT. Constrained minimization problems. U.S.S.R. Comput. Math. Phys. 1966;6(5):1-50.
(27) Bertsekas DP. Nonlinear programming. Athenas Scientific 1995;22-75 and 223-72.
(28) Bertsekas DP. On the Goldstein - Levitin - Polyak Gradient Projection Method. IEEE Transactions on Automatic Control 1976;21(2):174-84.
(29) Bertsekas DP. Projected Newton methods for optimization problems with simple constraints. SIAM J. Control and Optimization 1982;20(2):221-46.
(30) Gafni EM, Bertsekas DP. Convergence of a Gradient Projection Method. Massachusetts Institute of Technology. Laboratory for Information and Decision Systems 1982;1-12.
(31) Dunn JC. On the convergence of projected gradient processes to singular critical points. J. Optim. Theory. Appl 1987;55(2):203-16.
(32) Burke JV, More JJ. On the identification of active constraints. SIAM J. Numer. Anal 1988;25(5):1197-211.
(33) Calamai PH, More JJ. Projected Gradient Methods for linearly constrained problems. Mathematical Programming. Springer-Verlag, New York 1987;39(1):93-116.
(34) Xu HK. Averaged Mappings and the Gradient-Projection Algorithm. J. Optim. Theory. Appl. Springer 2011;150:360-78.
(35) Luenberger DG. A Combined penalty function and gradient projection method for nonlinear programming. J. Optim. Theory. App 1974;14(5):477-95.
(36) Soman KP, Ramanathan R. Digital Signal and Image processing- The Sparse Way. Isa Publishers. Elsevier, New Delhi, 2012;408-16.