[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