3D volume extraction of cerebrovascular structure on brain magnetic resonance angiography data sets
Author(s)
Do-Yeon Kim*
Affiliation(s)
Department of Computer Engineering, Sunchon National University, Sunchon, Republic of Korea.
Department of Computer Engineering, Sunchon National University, Sunchon, Republic of Korea.
ABSTRACT
The use of computers in facilitating their processing and analysis has become necessary with the increaseing size and number of medical images. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest, which are called image segmentation, play a vital role in numerous biomedical imaging applications such as the quantification of tissue volumes, diagnosis, localization of pathology, study of anatomical structure, treatment planning, and computer-integrated surgery. In this paper, a 3D volume extraction algorithm was proposed for segmentation of cerebrovascular structure on brain MRA data sets. By using a priori knowledge of cerebrovascular structure, multiple seed voxels were automatically identified on the initially thresholded image. In the consideration of the preserved voxel connectivity—which is defined as 6-connectivity with joint faces, 18-connectivity with joint edges, and 26-connectivity with joint corners— the seed voxels were grown within the cerebrovascular structure area throughout 3D volume extraction process. This algorithm provided better segmentation results than other segmentation methods such as manual, and histogram thresholding approach. This 3D volume extraction algorithm is also applicable to segment the tree-like organ structures such as renal artery, coronary artery, and airway tree from the medical imaging modalities.
The use of computers in facilitating their processing and analysis has become necessary with the increaseing size and number of medical images. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest, which are called image segmentation, play a vital role in numerous biomedical imaging applications such as the quantification of tissue volumes, diagnosis, localization of pathology, study of anatomical structure, treatment planning, and computer-integrated surgery. In this paper, a 3D volume extraction algorithm was proposed for segmentation of cerebrovascular structure on brain MRA data sets. By using a priori knowledge of cerebrovascular structure, multiple seed voxels were automatically identified on the initially thresholded image. In the consideration of the preserved voxel connectivity—which is defined as 6-connectivity with joint faces, 18-connectivity with joint edges, and 26-connectivity with joint corners— the seed voxels were grown within the cerebrovascular structure area throughout 3D volume extraction process. This algorithm provided better segmentation results than other segmentation methods such as manual, and histogram thresholding approach. This 3D volume extraction algorithm is also applicable to segment the tree-like organ structures such as renal artery, coronary artery, and airway tree from the medical imaging modalities.
Cite this paper
Kim, D. (2012) 3D volume extraction of cerebrovascular structure on brain magnetic resonance angiography data sets. Journal of Biomedical Science and Engineering, 5, 574-579. doi: 10.4236/jbise.2012.510070.
Kim, D. (2012) 3D volume extraction of cerebrovascular structure on brain magnetic resonance angiography data sets. Journal of Biomedical Science and Engineering, 5, 574-579. doi: 10.4236/jbise.2012.510070.
References
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[1] Biller, J. and Thies, W.H. (2000) When to operate in carotid artery disease. American Family Physician, 61, 400-410. [2] Pham, D.L., Xu, C. and Prince, J.L. (2000) Current method in medical image segmentation. Annual Review of Biomedical Engineering, 2, 315-337. [3] Kirbas, C. and Quek, F. (2004) A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 36, 81-121. [4] Wilson, D.L. and Nobel, J.A. (1997) Segmentation of cerebral vessels and aneurysms MR angiography data. Image Processing Medical Imaging Conference, 1230, 423-428. [5] Wilson, D.L. and Nobel, J.A. (1999) An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Transactions on Medical Imaging, 18, 938-945. [6] Chung, A.C.S., Nobel, J.A. and Summers, P. (2002) Fus ing speed and phase information for vascular segmentation of phase contrast MR angiograms. Medical Image Analysis, 6, 109-128. [7] Chung, A.C.S., Nobel, J.A. and Summers, P. (2004) Vascular segmentation of phase contrast magnetic resonance angiograms based on statistical mixture modeling and local phase coherence. IEEE Transaction on Medical Imaging, 23, 1409-1507. [8] Hassouna, M.S., Farag, A.A., Hushek, S. and Moriarty, T. (2006) Cerebrovascular segmentation from TOF using stochastic models. Medical Image Analysis, 10, 2-18. [9] Umbaugh, S.E. (1998) Computer Vision and Image Processing. Prentice Hall, New Jersey. [10] Jain, A.K. (1989) Fundamentals of Digital Image Processing. Prentice Hall, New Jersey. [11] Calotto, M.J. (1987) Histogram analysis using a scale-space approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 121-129. [12] Parker, J.R. (1997) Algorithm for image processing and computer vision. John-Wiley & Sons, New York. [13] Sonka, M., Hlavac, V. and Boyle, R. (1999) Image processing, analysis, and machine vision. PWS Publishing, Pacific Grove. [14] Castleman, K.R. (1996) Digital image processing. Prentice-Hall, New Jersey. [15] Adams, R. and Bischof, L. (1994) Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 641-647. [16] Mehnert, A. and Jackway, P. (1997) An improved seeded region growing algorithm. Pattern Recognition Letters, 18, 1065-1071. [17] Vaidyanathan, M., Clarke, L.P., Hall, L.O., Heidtman, C. and Velthuizen, R. (1997) Monitoring brain tumor response to therapy using MRI segmentation. Magnetic Resonance Imaging, 15, 323-334. [18] Wust, P., Gellermann, J., Beier, J., Wegner, S. and Tilly, W. (1998) Evaluation of segmentation algorithms for generation of patient models in radiofrequency hyperthermia. Physics in Medicine and Biology, 43, 3295-3307. [19] Lei, T. and Sewchand, W. (1992) Statistical approach to X-ray CT imaging and its applications in image analysis. IEEE Transaction on Medical Imaging, 11, 62-69. [20] Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J. and Evans, A.C. (1998) Design and construction of a realistic digital brain phantom. IEEE Transaction on Medical Imaging, 17, 463-468. doi:10.1109/42.712135 [21] Zubal, I.G., Harrell, C.R. and Smith, E.O. (1994) Computerized 3-dimensional segmented human anatomy. Medical Physics, 21, 299-302. doi:10.1118/1.597290 [22] Zaidi, H. (1999) Relevance of accurate Monte Carlo modeling in nuclear medical imaging. Medical Physics, 26, 574-608. doi:10.1118/1.598559 [23] Kim, D.Y. and Park, J.W. (under review). Geometric modeling of cervical artery and digital phantom implementation on tomographic image sequence. Journal of Visual Communication and Image Representation. [24] Kim, D.Y. and Park, J.W. (2005) Connectivity-based lo- cal adaptive thresholding for carotid artery segmentation using MRA images. Image and Vision Computing, 23, 1277-1287. doi:10.1016/j.imavis.2005.09.005 [25] Watt, A. (1999) 3D computer graphics. Addison-Wesley, Boston. [26] Noordmans, H.J., Voort, A. and Smeulders, A. (2000) Spectral volume rendering. IEEE Transactions on Visualization and Computer Graphics, 6, 196-207. doi:10.1109/2945.879782 [27] Fan, J., Yau, K.Y., Elmagarmid, A.K. and Aref, W.G. (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transaction on Image Processing, 10, 1454-1466. doi:10.1109/83.951532 [28] Cheng, S.C. (2003) Region-growing approach to colour segmentation using 3-D clustering and relaxation labeling. IEEE Proceedings—Vision Image and Signal Processing, 150, 270-276. doi:10.1049/ip-vis:20030520 [29] Zhou, Y., Starkey, J. and Mansinha, L. (2004) Segmentation of petrographic images by integrating edge detection and region growing. Computer & Geosciences, 30, 817- 831. doi:10.1016/j.cageo.2004.05.002 [30] Lira, J. and Maletti, G. (2002) A supervised contextual classifier based on a region-growth algorithm. Computers & Geosciences, 28, 951-959. doi:10.1016/S0098-3004(02)00017-1 [31] Wan, S.Y. and Higgins, W.E. (2003) Symmetric region growing. IEEE Transaction on Image Processing, 12, 1007-1015. doi:10.1109/TIP.2003.815258 [32] Lu, Y., Jiang, T. and Zang, Y. (2003) Region growing method for the analysis of functional MRI data. Neuro Image, 20, 455-465. doi:10.1016/S1053-8119(03)00352-5 [33] Grinias, I. and Tziritas, G. (2001) A semi-automatic seeded region growing algo-rithm for video object localization and tracking. Signal Processing: Image Communication, 16, 977-986. doi:10.1016/S0923-5965(01)00014-5 [34] Muller, C.R., Peyrin, F., Carrillon, Y. and Odet, C. (2002) Automated 3D region growing algorithm based on an as sessment function. Pattern Recognition Letter, 23, 137- 150. doi:10.1016/S0167-8655(01)00116-7