Image Clustering Framework for BAVM Segmentation in 3DRA Images: Performance Analysis
Brain ArterioVenous Malformation (BAVM) is an abnormal tangle of brain blood vessels where arteries shunt directly into veins with no intervening capillary bed which causes high pressure and hemorrhage risk. The success of treatment by embolization in interventional neuroradiology is highly dependent on the accuracy of the vessels visualization. In this paper the performance of clustering techniques on vessel segmentation from 3- D rotational angiography (3DRA) images is investigated and a new technique of segmentation is proposed. This method consists in: preprocessing step of image enhancement, then K-Means (KM), Fuzzy C-Means (FCM) and Expectation Maximization (EM) clustering are used to separate vessel pixels from background and artery pixels from vein pixels when possible. A post processing step of removing false-alarm components is applied before constructing a three-dimensional volume of the vessels. The proposed method was tested on six datasets along with a medical assessment of an expert. Obtained results showed encouraging segmentations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330683Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1444
 J. Byrne, "Cerebrovascular malformations," Eur. Radiol., 15: 448-452, 2005.
 H. Mast, JP. Mohr, A. Osipov, J. Pile-Spellman, RS. Marshall, RM. Lazar, BM. Stein, WL. Young, "ÔÇÿSteal- is an unestablished mechanism for the clinical presentation of cerebral ateriovenous malformations," Stroke 26: 1215-1220, 1995.
 J. Pik J, MK. Morgan, "Microsurgery for small arteriovenous malformations of the brain: Results of 110 consecutive cases," Neurosurgery, 47: 571-577, 2000.
 YP. Gobin, A. Laurent, L. Merienne, et al, "Treatment of brain arteriovenous malformations by embolization and radiosurgery," J Neurosurg 85: 19-28, 1996.
 M. Schlienger, D. Atlan, D. Lefkopoulos, et al, "Linac radiosurgery for cerebral arteriovenous malformations: results in 169 patients," Int J Radiat Oncol Biol Phys 46: 1135-42, 2000.
 JY Gauvrit, C. Oppenheim, F. Nataf, et al, "Three-dimensional dynamic magnetic resonance angiography for the evaluation of radiosurgically treated cerebral arteriovenous malformations," Eur Radiol 16: 583-591, 2006.
 L. Remonda, P. Senn, A. Barth, M. Arnold, KO. Lovblad, G. Schroth, "Contrast-enhanced 3D MR angiography of the carotid artery: comparison with conventional digital subtraction angiography," Am J Neuroradiol 23: 213-219, 2002.
 PC. Sanelli, MJ. Mifsud, PE. Stieg, "Role of CT angiography in guiding management decisions of newly diagnosed and residual arteriovenous malformations," Am J Roentgenol 183: 1123-1126, 2004.
 RM. Friedlander, "Arteriovenous malformations of the brain," New England Journal of Medicine, 356(26): 2704-2712, 2007.
 C. Cavedon, "Three-dimensional rotational angiography (3DRA) adds substatial information to radiosurgery treatment planning of AVM's compared to angio-CT and angio-MR," Med Phys 31(8): 2181-2, 2004.
 X. Combaz, O. Levrier, J. Moritz, J. Mancini, JM. Regis, JM. Bartoli, NJ. Girard, "Three-dimensional rotational angiography in the assessment of the angioarchitecture of brain arteriovenous malformations," Neuroradiol. 38(3): 167-74, 2011.
 Z. Yaniv, K. Cleary, "Computer Aided Interventions and Medical Robotics, Image-guided procedures: A review,", Image Science and Information Systems Center, Georgetown University Medical Center, Washington, DC, Tech. Rep., 2006.
 D. Lesage, E.D. Angelini, I. Bloch, G. Funka-Lea, "A review of 3D Vessel Lumen Segmentation Techniques: Models, Features and Extraction Schemes," Medical Image Analysis 13(6): 819-845, 2009.
 M. Piccinelli, A. Veneziani, D.A. Steinman, A. Remuzzi, L. Antiga, "A Framework for Geometric Analysis of Vascular Structures: application to Cerebral Aneurysms," IEEE Transactions on Medical Imaging, 1141- 1155, 2009.
 D. Nain, A. Yezzi, G. Turk, "Vessel segmentation using a shape driven flow," Intl. Conf. on Medical Image Computing and Comp. Ass. Intervention (MICCAI), 51-59, 2003.
 M. Donizelli, "Region-oriented segmentation of vascular structures from dsa images using mathematical morphology and binary region growing", Bildverarbeitung f├╝r die Medizin, 12: CEUR Workshop proceeding, 1998
 F. Weiler, C. Rieder, C. A. David, C. Wald, and H. K. Hahn, "Avmexplorer: Multi-volume visualization of vascular structures for planning of cerebral avm surgery," Eurographics Association, Llandudno, UK, 9- 12, 2011.
 D. Babin, E. Vansteenkiste, A. Pizurica and W. Philips, "Segmentation of brain blood vessels using projections in 3-D CT angiography images," IEEE EMBS, 8475-8478, 2011.
 M. Hernandez, AF. Frangi, "Non-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA," Med Image Anal 11: 224-241, 2007.
 C.W. Chen, J. Luo, K.J. Parker, "Image segmentation via adaptive Kmean clustering and knowledge based morphological operations with biomedical applications", IEEE Transactions on Image Processing, 7(12): 1673-1683, 1998.
 H. Ng, S. Ong, K. Foong, P. Goh, and W. Nowinski, "Image segmentation using k-means clustering and improved watershed algorithm," IEEE Southwest Symp. Image Anal. Interpretation, 61- 65, 2006.
 A. S. Binsamma and R. AbdulSalam, "Adaptation of K Means Algorithm for Image Segmentation," International Journal of Signal Processing, 5(4): 270-274, 2009.
 S. Tatiraju and A. Mehta, "Image segmentation using k-means clustering, EM and normalized cuts," UC Irvine, 2008.
 B.Sathya, R.Manavalan, "Image Segmentation by Clustering Methods: Performance Analysis," International Journal of Computer Applications, 29(11): 0975 - 8887, 2011.
 M.C.J. Christ and R.M.S. Parvathi, "Fuzzy c-means algorithm for medical image segmentation," Electronics Computer Technology (ICECT), 2011 3rd International Conference on , 4: 33-36, 2011.
 S. Naz, H. Majeed, H. Irshad, "Image segmentation using fuzzy clustering: A survey," Emerging Technologies (ICET), 6th International Conference 181-186, 2010.
 M. Shasidhar, V.S. Raja, B.V. Kumar, "MRI Brain Image Segmentation Using Modified Fuzzy C-Means Clustering Algorithm," Communication Systems and Network Technologies (CSNT), International Conference, 473-478, 2011.
 T. Kalaiselvi, K. Somasundaram, "Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in MRI of head scans," Humanities, Science & Engineering Research (SHUSER), 2011 International Symposium, 149-154, 2011.
 T.K. Moon, "The expectation-maximization algorithm," Signal Processing Magazine, IEEE , 13(6): 47-60, 1996.
 Y. Zhang, M. Brady and S. Smith, "Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm," IEEE Transactions on Medical Imaging, 20(1): 45-57, 2001.
 M.S. Nair, R. Rajasree, J. John, and M. Wilscy , "Expectation- Maximization with Distance Measure for Color Image Segmentation," IEEE Region 10 and the Third international Conference on Industrial and Information Systems, 1-5, 2008.
 R. H. Chan, C. Ho, M. Nikolova, "Salt-and-Pepper Noise Removal by Median-type Noise Detectors and Detailpreserving Regularization," IEEE Transactions on Image Processing, 14: 1479-1485, 2005.
 L. Ding, A. Goshtasby, "On the Canny edge detector", Pattern Recognition, 34: 721-725, 2001.
 P. Lacroute, "Analysis of a parallel volume rendering system based on the shear-warp factorization," IEEE Transactions on Visualization and Computer Graphics, 2(3): 218- 231, 1996.
 H. Bogunovic, A. G. Radaelli, M. D. Craene, D. Delgado, and A. F. Frangi, "Image intensity standardization in 3d rotational angiography and its application to vascular segmentation," Proc. SPIE Med. Imag., 6914: 91419-91419, 2008.