Abstract
A supervised method is presented for the detection and segmentation of ribs in computed tomography (ct) data. In a first stage primitives are extracted that represent parts of the centerlines of elongated structures. Each primitive is characterized by a number of features computed from local image structure. For a number of training cases, the primitives are labeled by a human observer into two classes (rib vs. non-rib). This data is used to train a classifier. Now, primitives obtained from any image can be labeled automatically. In a final stage the primitives classified as ribs are used to initialize a seeded region growing process to obtain the complete rib cage.
The method has been tested on 20 images. Of the primitives, 96.9% is classified correctly. The results of the final segmentation are satisfactory.
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References
Summers, R.M.: Road maps for advancement of radiologic computer-aided detection in the 21st century. Radiology 229(1), 11–13 (2003)
Prokop, M., Galanski, M. (eds.): Spiral and multislice computed tomography of the body. Thieme, Stuttgart (2003)
Aylward, S.R., Bullitt, E., Pizer, S., Eberly, D.: Intensity ridge and widths for tubular object segmentation and description. In: Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 131–138 (1996)
Aylward, S.R., Bullit, E.: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans. Med. Imag. 21(2), 61–75 (2002)
Kim, D., Kim, H., Kang, H.S.: An object-tracking segmentation method: vertebra and rib segmentation in CT images. In: SPIE Proceedings: Medical Imaging 2002, vol. 4684, pp. 1662–1671 (2002)
Kang, Y., Engelke, K., Kalender, W.A.: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Trans. Med. Imag. 22(5), 586–598 (2003)
Dam, E.B., Nielsen, M.: Non-linear diffusion for interactive multi-scale watershed segmentation. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 216–225. Springer, Heidelberg (2000)
Florack, L.M.J.: Image structure. Kluwer Academic Press, Dordrecht (1997)
Duda, R.O., Hart, P.E., Stork, H.G.: Pattern classification, 2nd edn. Wiley-Interscience, New York (2001)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. J. ACM 45(6), 891–923 (1998)
Metz, C.E.: Basic principles of roc analysis. Seminars in Nucl. Med. 8(4), 283–298 (1978)
Whitney, A.W.: A direct method of non parametric measurement selection. IEEE Trans. Comput. 20(9), 1100–1103 (1971)
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans Pattern Anal. Machine Intell. 6(16), 641–647 (1994)
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Staal, J., van Ginneken, B., Viergever, M.A. (2004). Automatic Rib Segmentation in CT Data. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_17
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DOI: https://doi.org/10.1007/978-3-540-27816-0_17
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