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Segmentation and 3D Visualization of Medical Image : An Overview
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 Title & Authors
Segmentation and 3D Visualization of Medical Image : An Overview
Kang, Jiwoo; Kim, Doyoung; Lee, Sanghoon;
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In this paper, an overview of segmentation and 3D visualization methods are presented. Commonly, the two kinds of methods are used to visualize organs and vessels into 3D from medical images such as CT(A) and MRI - Direct Volume Rendering (DVR) and Iso-surface Rendering (IR). DVR can be applied directly to a volume. It directly penetrates through the volume while it determines which voxels are visualizedbased on a transfer function. On the other hand, IR requires a series of processes such as segmentation, polygonization and visualization. To extract a region of interest (ROI) from the medical volume image via the segmentation, some regions of an object and a background are required, which are typically obtained from the user. To visualize the extracted regions, the boundary points of the regions should be polygonized. In other words, the boundary surface composed of polygons such as a triangle and a rectangle should be required to visualize the regions into 3D because illumination effects, which makes the object shaded and seen in 3D, cannot be applied directly to the points.
Medical Image Processing;Segmentation;Polygonization;3D Visualization;Direct Volume Rendering;Iso-surface Rendering;
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Journal of International Society for Simulation Surgery, 2015. vol.2. 1, pp.13-16 crossref(new window)
Preim, B., & Oeltze, S. (2008). 3D visualization of vasculature: an overview. In Visualization in medicine and life sciences (pp. 39-59). Springer Berlin Heidelberg

McAuliffe, M. J., Lalonde, F. M., McGarry, D., Gandler, W., Csaky, K., & Trus, B. L. (2001). Medical image processing, analysis and visualization in clinical research. In Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on (pp. 381-386). IEEE

Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation 1. Annual review of biomedical engineering, 2(1), 315-337 crossref(new window)

Prasantha, H. S., Shashidhara, H. L., Murthy, K. N. B., & Madhavi, L. G. (2010). Medical image segmentation. International Journal on Computer Science and Engineering, 2(4), 1209-1218

Kim, Y. M., Baek, S. E., Lim, J. S., & Hyung, W. J. (2013). Clinical application of image-enhanced minimally invasive robotic surgery for gastric cancer: a prospective observational study. Journal of Gastrointestinal Surgery, 17(2), 304-312 crossref(new window)

Kumano, S., Tsuda, T., Tanaka, H., Hirata, M., Kim, T., Murakami, T., et al. (2007). Preoperative evaluation of perigastric vascular anatomy by 3-dimensional computed tomographic angiography using 16-channel multidetector-row computed tomography for laparoscopic gastrectomy in patients with early gastric cancer. Journal of computer assisted tomography, 31(1), 93-97 crossref(new window)

Matsuki, M., Kani, H., Tatsugami, F., Yoshikawa, S., Narabayashi, I., Lee, S. W., et al. (2004). Preoperative assessment of vascular anatomy around the stomach by 3D imaging using MDCT before laparoscopy-assisted gastrectomy. American Journal of Roentgenology, 183(1), 145-151 crossref(new window)

Miyaki, A., Imamura, K., Kobayashi, R., Takami, M., Matsumoto, J., & Takada, Y. (2012). Preoperative assessment of perigastric vascular anatomy by multidetector computed tomography angiogram for laparoscopy-assisted gastrectomy. Langenbeck's Archives of Surgery, 397(6), 945-950 crossref(new window)

Natsume, T., Shuto, K., Yanagawa, N., Akai, T., Kawahira, H., Hayashi, H., et al. (2011). The classification of anatomic variations in the perigastric vessels by dual-phase CT to reduce intraoperative bleeding during laparoscopic gastrectomy. Surgical endoscopy, 25(5), 1420-1424 crossref(new window)

Usui, S., Hiranuma, S., Ichikawa, T., Maeda, M., Kudo, S. E., & Iwai, T. (2005). Preoperative imaging of surrounding arteries by threedimensional CT: is it useful for laparoscopic gastrectomy?. Surgical Laparoscopy Endoscopy & Percutaneous Techniques, 15(2), 61-65 crossref(new window)

Rottger, S., Kraus, M., & Ertl, T. (2000, October). Hardware-accelerated volume and isosurface rendering based on cell-projection. In Proceedings of the conference on Visualization'00 (pp. 109-116). IEEE Computer Society Press

Parker, S., Shirley, P., Livnat, Y., Hansen, C., & Sloan, P. P. (1998, October). Interactive ray tracing for isosurface rendering. In Proceedings of the conference on Visualization'98 (pp. 233-238). IEEE Computer Society Press

Drebin, R. A., Carpenter, L., & Hanrahan, P. (1988, June). Volume rendering. In ACM Siggraph Computer Graphics (Vol. 22, No. 4, pp. 65-74). ACM crossref(new window)

Lorensen, W. E., & Cline, H. E. (1987, August). Marching cubes: A high resolution 3D surface construction algorithm. In ACM Siggraph Computer Graphics (Vol. 21, No. 4, pp. 163-169). ACM crossref(new window)

Bloomenthal, J. (1988). Polygonization of implicit surfaces. Computer Aided Geometric Design, 5(4), 341-355 crossref(new window)

Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., & Seidel, H. P. (2005, July). Multi-level partition of unity implicits. In ACM SIGGRAPH 2005 Courses (p. 173). ACM

Surazhsky, V., & Gotsman, C. (2003, June). Explicit surface remeshing. In Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing (pp. 20-30). Eurographics Association

Sifri, O., Sheffer, A., & Gotsman, C. (2003). Geodesic-based surface remeshing. In In Proc. 12th Intnl. Meshing Roundtable

Hadwiger, M., Kniss, J. M., Rezk-Salama, C., & Weiskopf, D. (2006). Real-time volume graphics (pp. I-XVII). Natick: Ak Peters

Dougherty, G. (2009). Digital image processing for medical applications. Cambridge University Press

Smistad, E., Elster, A. C., & Lindseth, F. (2011). Fast surface extraction and visualization of medical images using opencl and gpus. In The Joint Workshop on High Performance and Distributed Computing for Medical Imaging (Vol. 2011)