Extraction of Vascular Structure in 3D Cardiac CT Images by Using Object/Background Normalization

Pattern Recognition and Image Analysis - Tập 30 - Trang 237-246 - 2020
S. Ye1, D. Hancharou2, H. Chen1, A. Nedzvedz3,4, H. Lv1, S. Ablameyko3,4
1Zhejiang Shuren University, Hangzhou, People’s Republic of China
2Flo Company, Minsk, Republic of Belarus
3Belarusian State University, Minsk, Republic of Belarus
4United Institute of Informatics Problems of National Academy of Sciences, Minsk, Republic of Belarus

Tóm tắt

The vessel structures of the blood circulatory system are one of the most complex structures of the human body. Modern computed tomography techniques allow acquiring high resolution images, but at the same time, the number of artifacts in output images is quite high. They may affect diagnostic result and may obscure or simulate pathology. The idea of our method is to represent a 3D computed tomography image as a combination of vascular structure and background that has normal distribution in some neighborhood. Locally adaptive non-linear filters decrease global difference between bright and dark voxels, even if it produces better local contrast. Luminosity and contrast are observed from image background and are used for normalization of the whole image. After making background normalization at each layer, we merge layers and reconstruct vessels structure. The proposed method has been tested on real cardiac CT images, the test results show that high quality 3D structures are reconstructed, without requiring a priori knowledge or user interaction. The tested dataset has been made publicly available. The proposed approach can be applied to denoising computed tomography images, enhancing of contrast in lesion areas without changing topology of initial vessel structures.

Tài liệu tham khảo

The National Statistical Committee of the Republic of Belarus, “Demographic Yearbook of the Republic of Belarus,” 323 p. (2003). World Health Organization, “The top 10 causes of death.” http://www.who.int/mediacentre/factsheets/fs310/en/ F. E. Boas and D. Fleischmann, “CT artifacts: Causes and reduction techniques,” Imaging Med. 4 (2), 229–240 (2012). C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Comput. Surv. 36 (2), 81–121 (2004). D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, “A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes,” Med. Image Anal. 13 (6), 819–845 (2009). E. Bullitt and S. R. Aylward, “Analysis of time-varying images using 3d vascular models,” in Proc. 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery (Washington, DC, USA, 2001), IEEE, pp. 9–14. A. M. Yatchenko, A. S. Krylov, A. V. Gavrilov, and I. V. Arkhipov, “3D liver vessels model design using CT data,” in Proc. 19th Int. Conf. on Computer Graphics and Vision (GraphiCon’2009) (Moscow, Russia, 2009), pp. 344−347 [in Russian]. R. Grothausmann, M. Kellner, M. Heidrich, et al., “Method for 3D airway topology extraction,” Comput. Math. Methods Med. 2015, Article ID 127010 (2015). https://doi.org/10.1155/2015/127010 J. F. Carrillo, M. Orkisz, and M. Hernández Hoyos, “Extraction of 3D vascular tree skeletons based on the analysis of connected components evolution,” in Computer Analysis of Images and Patterns, Proc. 11th Int. Conf. CAIP 2005 (Versailles, France, 2005), Ed. by A. Gagalowicz and W. Philips, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2005), Vol. 3691, pp. 604–611. G. Yang, P. Kitslaar, M. Frenay, et al., “Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography,” Int. J. Cardiovasc. Imaging 28 (4), 921–933 (2012). R. Bates, B. Irving, B. Markelc, et al., “Extracting 3D vascular structures from microscopy images using Convolutional Recurrent Networks,” arXiv preprint arXiv:1705.09597 (2017). https://arxiv.org/abs/1705.09597 H. S. Bhadauria, S. S. Bisht, and A. Singh, “Vessels extraction from retinal images,” IOSR J. Electron. Commun. Eng. 6 (3), 79–82 (2013). Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30 (8), 2040–2051 (2003). C. T. Metz, M. Schaap, A. C. Weustink, et al., “Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach,” Med. Phys. 36 (12), 5568–5579 (2009). D. Hancharou, A. Nedzved, and S. Ablameyko, “3D Distance transform and its application for processing of medical images,” J. Inf., Control Manage. Syst. 8 (2), 43–53 (2010). D. Hancharou, A. Nedzved, and S. Ablameyko, “Skeletonization algorithm of high resolution vascular data,” in Pattern Recognition and Information Processing (PRIP’2014), Proc. 12th Int. Conf. (Minsk, Belarus, 2014), pp. 76–80.