Automated 3-D Tissue Segmentation Via Clustering
Keywords:medical images, magnetic resonance imaging, computer-assisted tomography, clustering, DBSCAN
Generation of 3-D tissue models from medical imagery is useful for surgical planning and computer simulations, but often requires some amount of manual effort. In this work, we use the clustering algorithm, DBSCAN, in concert with a 3-D buildup procedure to automatically generate 3-D surface models of the brain and lungs from computed tomography head and chest scans, respectively. Extensions to other tissue types such as heart and liver are demonstrated for contrast-enhanced imagery.
(1) A. N. Ranslow, et al., Microstructural analysis of porcine skull bone subjected to impact loading. ASME 2015 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, 2015.
(2) M. Ester, et al., A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, Vol. 96, 1996, pp. 226–231.
(3) M. E. Celebi, Y. A. Aslandogan, P. R. Bergstresser, Mining biomedical images with density-based clustering. Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on, Vol. 1, IEEE, 2005, pp. 163–168.
(4) S. K. Bandyopadhyay, T. U. Paul, Segmentation of brain tumour from mri image analysis of k-means and dbscan clustering. International Journal of Research in Engineering and Science 1 (1) (2013) 48–57.
(5) W. Zhang, X. Zhang, J. Zhao, Y. Qiang, Q. Tian, X. Tang, A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise. PloS one 12 (9).
(6) L. Vincent, P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence (6) (1991) 583–598.
(7) Y. Tan, L. H. Schwartz, B. Zhao, Segmentation of lung lesions on ct scans using watershed, active contours, and markov random field. Medical physics 40 (4).
(8) R. Adams, L. Bischof, Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence 16 (6) (1994) 641–647.
(9) R. Pohle, K. D. Toennies, Segmentation of medical images using adaptive region growing. Proc. SPIE Medical Imaging, Vol. 4322, 2001, pp. 1337–1346.
(10) N. J. Mankovich, D. Samson, W. Pratt, D. Lew, J. Beumer 3rd, Surgical planning using three-dimensional imaging and computer modeling. Otolaryngologic Clinics of North America 27 (5) (1994) 875–889.
(11) T. M. Bücking, E. R. Hill, J. L. Robertson, E. Maneas, A. A. Plumb, D. I. Nikitichev, From medical imaging data to 3d printed anatomical models. PloS one 12 (5).
(12) K. H. Höhne, W. A. Hanson, Interactive 3d segmentation of mri and ct volumes using morphological operations. Journal of computer assisted tomography 16 (2) (1992) 285–294.
(13) A. Fedorov, et al., 3d slicer as an image computing platform for the quantitative imaging network. Magnetic resonance imaging 30 (9) (2012) 1323–1341.
(14) Materialise, Mimics software, http://www.materialise.com (2016).
(15) L. Massoptier, S. Casciaro, A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from ct scans. European radiology 18 (8) (2008) 1658.
(16) K. Subburaj, B. Ravi, High resolution medical models and geometric reasoning starting from ct/mri images. 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, IEEE, 2007, pp. 441–444.
(17) T. Heimann, I. Wolf, H.-P. Meinzer, Active shape models for a fully automated 3d segmentation of the liver–an evaluation on clinical data. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006 (2006) 41–48.
(18) Q. Ye, W. Gao, W. Zeng, Color image segmentation using density-based clustering. Multimedia and Expo, 2003. ICME’03. Proceedings. 2003 International Conference on, Vol. 2, IEEE, 2003, pp. II–401.
(19) W. E. Lorensen, H. E. Cline, Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, Vol. 21, ACM, 1987, pp. 163–169.
(20) K. Clark, et al., The cancer imaging archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging 26 (6) (2013) 1045–1057.
(21) W. R. Bosch, W. L. Straube, J. W. Matthews, J. A. Purdy, Data from head-neck cetuximab. Tech. rep., The Cancer Imaging Archive (2015).
(22) O. Grove, et al., Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS one 10 (3 (2015) e0118261.
(23) A. P. Reeves, Y. Xie, S. Liu, Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation. Journal of Medical Imaging 4 (2) (2017) 024505–024505.
(24) W. J. Schroeder, B. Lorensen, K. Martin, The visualization toolkit: an object-oriented approach to 3D graphics, Kitware, 2004.
(25) G. N. Hounsfield, Computerized transverse axial scanning (tomography): Part 1. Description of system. The British journal of radiology 46 (552) (1973) 1016–1022.
(26) C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images. Computer Vision, 1998. Sixth International Conference on, IEEE, 1998, pp. 839–846.
(27) E. Jones, et al., SciPy: Open source scientific tools for Python (2001). URL http://www.scipy.org/
(28) J. Ahrens, B. Geveci, C. Law, Paraview: An end-user tool for large data visualization, The Visualization Handbook 717.
(29) I. Wald, G. P. Johnson, J. Amstutz, C. Brownlee, A. Knoll, J. Jeffers, J. Günther, P. Navratil, Ospray-a cpu ray tracing framework for scientific visualization. IEEE transactions on visualization and computer graphics 23 (1) (2017) 931–940.
(30) D. Comaniciu, P. Meer, Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence 24 (5) (2002) 603–619.