Abstract
The paper discusses segmentation of medical images depicting aortic aneurysms and atherosclerotic changes in coronary vessels. The region growing method was deployed for segmentation. Before segmentation with the aforementioned method, the images were subjected to edging in order to acquire significant information, such as the size of the analyzed structure and pixel distribution. Edging paired with the region growing method ensures proper isolation of pixels with the same intensity, without the unwanted pixel overflow. In order to verify the method, results obtained by various authors were referred to, and a statistical analysis was performed to calculate the Dice coefficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Agamy, A., Osman, N., Fahmy, A.: Segmentation of ascending and descending aorta from magnetic resonance flow images. In: Biomedical Engineering Conference (CIBEC), pp. 41–44 (2010)
Avila-Montes, O., Kurkure, U., Nakazato, R., Berman, D., Dey, D.: Segmentation of the thoracic aorta in noncontrast cardiac CT images. IEEE J. Biomed. Health Inform. 17(5), 936–949 (2013)
Ben Ayed, I., Wang, M., Miles, B., Garvin, G.J.: TRIC: trust region for invariant compactness and its application to abdominal aorta segmentation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 381–388. Springer, Heidelberg (2014)
Babin, D., Devos, D., Pizurica, A.: Robust segmentation methods with an application to aortic pulsewave velocity calculation. Comput. Med. Imaging Graph. 38, 179–189 (2014)
Benmansour, F., Cohen, L.: A new interactive method for coronary arteries segmentation based on tubular anisotropy. In: International Symposium on Biomedical Imaging: From Nano to Macro. IEEE (2009)
Bruijne, M., Ginneken, B., Niessen, W., Loog, M., Viergever, M.: Model-based segmentation of abdominal aortic aneurysms in CTA images. In: SPIE, vol. 5032 (2003)
Chen, S., Wang, T., Lee, W.: Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform. Biomed. Res. Int. 2015, 1–9 (2015). doi:10.1155/2015/798303. Article ID 798303. PMID 25648181
Egger, J., Freisleben, B., Setser, R., Renapuraar, R.: Aorta segmentation for stent simulation. In: CI2BM09 – MICCAI (2011)
Goyal, P., Goyal, K., Gupta, V.: Calcification detection in coronary arteries using image processing. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(8), 279–284 (2013). ISSN: 2277 128X
Goyal, P., Gupta, V., Goyal, K.: Segmentation of coronary arteries of heart. Int. J. Adv. Electr. Electron. Eng. 2(1), 93–98 (2013). ISSN: 2319-1112
Kovacs, T.,Cattin, P., Alkadhi, H.: Automatic segmentation of the vessel lumen from 3D CTA images of aortic dissection, Bildverarbeitung für die Medizin, pp. 161–165. Springer (2006)
Lara, D., Faria, A., Araújo, A., Menotti, D.: A novel hybrid method for the segmentation of thr coronary artery tree in 2D angiograms. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5(3), 45–64 (2013). doi:10.5121/ijcsit.2013.5304
Macía, I., Legarreta, J.H., Paloc, C., Graña, M., Maiora, J., García, G., de Blas, M.: Segmentation of abdominal aortic aneurysms in CT images using a radial model approach. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 664–671. Springer, Heidelberg (2009)
Okusz, I., Ünay, D., Kadpasaoglu, K.: A hybrid method for coronary artery stenoses detection and quantification in CTA images. In: MICCAI Workshop 3d Cardiovascular Imaging: A MICCAI Segmentation (2012)
Ozkan, H.: Segmentation of ascending and descending Aorta in CTA images. Int. J. Med. Health, Biomed. Bioeng. Pharm. Eng. 6(5), 451–453 (2012)
Piekar, E., Momot, A.: Gradient and polynomial approximation methods for medical image segmentation. J. Med. Imaging Health Inf. 5, 1337–1349 (2015)
Piekar, E., Szwarc, P., Sobotnicki, A., Momot, M.: Application of region growing method to brain tumor segmentation-preliminary results. J. Med. Inf. Technol. 22 (2013). ISSN 1642-6037
Roussona, M., Baib, Y., Xua, C., Sauera, F.: Probabilistic minimal path for automated esophagus segmentation. In: SPIE, vol. 6144 (2006)
Tek, H., Zheng, Y., Gulsun, M., Funka-Lea, G.: An automatic system for segmenting coronary arteries from CTA. In: MICCAI Workshop on Computing and Visualization for Intravascular Imaging (2011)
Zhao, F., Zhang, H., Wahle, A.: Automated 4D Segmentation of Aortic Magnetic Resonance Images. In: BMVC, pp. 247–256 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Sobotnicka, E., Sobotnicki, A., Horoba, K., Porwik, P. (2016). The Application of the Region Growing Method to the Determination of Arterial Changes. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-45246-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45245-6
Online ISBN: 978-3-319-45246-3
eBook Packages: Computer ScienceComputer Science (R0)