Abstract
This paper presents a deformable model based approach for automated segmentation of kidneys from tree dimensional (3D) abdominal CT images. Since the quality of an input image is very poor and noisy due to the large slice thickness, we use a deformable model represented by NURBS surface, which uses not only the gray level appearance of the target but also statistical information of the shape. A shape feature vector is defined to evaluate geometric character of the surface and its statistical information is incorporated into the deformable model through an energy formulation for deformation. Principal curvature on the model surface, which is invariant to rotation and translation, is adopted as a component of the vector. Furthermore, automated positioning procedure of an initial model is presented in this paper. We applied the proposed method to the 33 abdominal CT images whose slice thickness is 10mm and evaluated the effectiveness of the proposing method.
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Duncan, J., Ayache, N.: Medical Image Analysis: Progress Over Two Decades and Challenges Ahead, IEEE Trans. Patt. Anal. Mach. Intell., vol. 22 (2000) 85–106
McInerney, T., Terzopoulos, D.: Deformable Models in Medical Image Analysis: A Survey, Med. Imag. Anal. vol. 1(2) (1996) 91–108
Shimizu, A.: Segmentation of Medical Images Using Deformable Models: A Survey, Med. Imag. Tech. in Japan. vol. 20(1) (2002) 3–12
Cootes, F., Taylor, L., Cooper, H.: Active Shape Models;Their Training and Application, CVIU vol. 61(1) (1995) 38–59
Jacob, G., Noble, A., Blake, A.: Evaluating a Robust Contour Tracker on Echocardiographic Sequences, Med. Imag. Anal. vol. 3(3) (1998) 63–75
Fleute, M., Lavallee, M., Julliard, S.: Incorporating a Statistically Based Shape Model Into a System for Computer-Assisted Anterior Cruciate Ligament Surgery, Med. Imag. Anal. vol. 3(3) (1999) 209–222
Shen, D., Herskovits, E.H., Davatzikos, C.: An Adaptive-Focus Statistical Shape Model for Segmentation and Shape Modeling of 3-D Brain Structure, IEEE Trans. Med. Imag., vol. 20(4) (2001) 257–270
Hamarneh, G., McInerney, T., Terzopoulos, D.: Deformable Organisms For Automatic Medical Image Analysis, MICCAI (2001) 66–76
Staib, L.H., Duncan, J.S.: Boundary Finding with Parametrically Deformable Models, IEEE Trans. Patt. Anal. Mach. Intell., vol. 14(11) (1992) 1061–1075
Szekely, G., Kelemen, A.: Segmentation of 2-D and 3-D Objects From MRI Volume Data Using Constrained Elastic Deformations of Flexible Fourier Contour and Surface Models, Med. Imag.Anal., vol. 1(1) (1996) 19–34
Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K., Hanzawa, Y.: Segmentation of Kidney by Using Deformable Model, ICIP, vol. 3 (2001) 1059–1062
Terzopoulos, D., Qin, H.,: Dynamic NURBS with Geometric Constraints for Interactive Sculpting”, ACM Trans. Graphics, vol. 13(2) (1994) 103–136
Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K.: Development of Extraction Method of Kidneys From Abdominal CT Images Using 3-D Deformable Model, Trans. of IEICE in Japan, vol. J85(D-II) (2002) 140–148
Williams, D., Shan, D., Shan, M.,: A Fast Algorithm for Active Contours, CVGIP:Imag. Under., vol. 55(1) (1992) 14–26
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Tsagaan, B., Shimizu, A., Kobatake, H., Miyakawa, K. (2002). An Automated Segmentation Method of Kidney Using Statistical Information. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_69
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DOI: https://doi.org/10.1007/3-540-45786-0_69
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