Abstract
In this paper, we focus on automatic kidneys detection in 2D abdominal computed tomography (CT) images. Identifying abdominal organs is one of the essential steps for visualization and for providing assistance in teaching, clinical training and diagnosis. It is also a key step in medical image retrieval application. However, due to gray levels similarities of adjacent organs, contrast media effect and relatively high variation of organ’s positions and shapes, automatically identifying abdominal organs has always been a challenging task. In this paper, we present an original method, in a statistical framework, for fully automatic kidneys detection. It makes use of spatial and gray-levels prior models built using a set of training images. The method is tested on over 400 clinically acquired images and very promising results are obtained.
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Najjar, M., Ambroise, C., Cocquerz, J.P.: Image retrieval using mixture models and EM alogorithm. In: 13th Scandinavian Conf. SPIE, pp. 1114–1121 (2003)
Lee, C.C., Chung, P.C., Tsai, H.M.: Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules. IEEE Trans. Inf. Technol. Biomed. 7, 208–217 (2003)
Kobashi, M., Shapiro, L.G.: Knowledge-based organ identification from CT images. Pattern Recognit 28, 475–491 (1995)
Park, H., Bland, P.H., Meyer, C.R.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE TMI 22, 483–492 (2003)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, NY (1992)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE TMI 20, 45–57 (2001)
Boukerroui, D., Baskurt, A., Noble, J.A., Basset, O.: Segmentation of ultrasound images - multiresolution 2D and 3D algorithm based on global and local statistics. Pattern Recognit. Lett. 24, 779–790 (2003)
Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected-component labeling based on sequential local operations. Comput. Vis. Image Underst. 89, 1–23 (2003)
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Touhami, W., Boukerroui, D., Cocquerez, JP. (2005). Fully Automatic Kidneys Detection in 2D CT Images: A Statistical Approach. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_33
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DOI: https://doi.org/10.1007/11566465_33
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