Statistical-Based Segmentation of Bone Structures via Continuous Max-Flow Optimization

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• 作者：Jose-Antonio P茅rez Carrasco (17)
  Carmen Serrano Gotarredona (17)
  Cristina Su谩rez-Mej铆as (18)
  Bego帽a Acha-Pi帽ero (17)
  
• 关键词：Segmentation ; Convex relaxation ; Max ; flow
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：1
• 期：1
• 页码：201-208
• 全文大小：573 KB
• 参考文献：1. Feeman, T.G.: The mathematics of medical imaging: a beginner鈥檚 guide. Springer undergraduate texts in mathematics and technology. Springer (2010)
  2. Wang, L.I., et al.: Validation of bone segmentation and improved 3-D registration using contour coherency in CT data. IEEE Transactions on Medical Imaging ass="a-plus-plus">25(3), 324鈥?34 (2006) <a class="external" href="http://dx.doi.org/10.1109/TMI.2005.863834" target="_blank" title="It opens in new window">CrossRefa>
  3. Kang, Y., et al.: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Transactions on Medical Imaging ass="a-plus-plus">22(5), 586鈥?98 (2003) <a class="external" href="http://dx.doi.org/10.1109/TMI.2003.812265" target="_blank" title="It opens in new window">CrossRefa>
  4. Pardo, X.M., et al.: A snake for CT image segmentation integrating region and edge information. Image and Vision Computing ass="a-plus-plus">19(7), 461鈥?75 (2001) <a class="external" href="http://dx.doi.org/10.1016/S0262-8856(00)00092-5" target="_blank" title="It opens in new window">CrossRefa>
  5. Beveridge, J.R., et al.: Segmenting images using localized histograms and region merging. International Journal of Computer Vision ass="a-plus-plus">2(3), 311鈥?47 (1989) <a class="external" href="http://dx.doi.org/10.1007/BF00158168" target="_blank" title="It opens in new window">CrossRefa>
  6. Sebastian, T.B., et al.: Segmentation of carpal bones from CT images using skeletally coupled deformable models. Medical Image Analysis ass="a-plus-plus">7(1), 2鈥?5 (2003) <a class="external" href="http://dx.doi.org/10.1016/S1361-8415(02)00065-8" target="_blank" title="It opens in new window">CrossRefa>
  7. Cheng, Y., et al.: Automatic segmentation technique for acetabulum and femoral head in CT images. Pattern Recognition ass="a-plus-plus">46(11), 2969鈥?984 (2013) <a class="external" href="http://dx.doi.org/10.1016/j.patcog.2013.04.006" target="_blank" title="It opens in new window">CrossRefa>
  8. Cervinka, T., Hyttinen, J., Siev盲nen, H.: Accurate cortical bone detection in peripheral quantitative computed tomography images. In: Roa Romero, L.M. (ed.) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol. 41, pp. 289鈥?92. Springer, Heidelberg (2014) <a class="external" href="http://dx.doi.org/10.1007/978-3-319-00846-2_72" target="_blank" title="It opens in new window">CrossRefa>
  9. Aslan, M.S., Ali, A., Rara, H., Arnold, B., Farag, A.A., Fahmi, R., Xiang, P.: A novel 3D segmentation of vertebral bones from volumetric CT images using graph cuts. In: Bebis, G., et al. (eds.) ISVC 2009, Part II. LNCS, vol. 5876, pp. 519鈥?28. Springer, Heidelberg (2009) <a class="external" href="http://dx.doi.org/10.1007/978-3-642-10520-3_49" target="_blank" title="It opens in new window">CrossRefa>
  10. Shadid, W., Willis, A.: Bone fragment segmentation from 3D CT imagery using the Probabilistic Watershed Transform. In: Conference Proceedings - IEEE SOUTHEASTCON (2013)
  11. Calder, J., et al.: A variational approach to bone segmentation in CT images. In: Progress in Biomedical Optics and Imaging - SPIE (2011)
  12. Kratky, J., Kybic, J.: Three-dimensional segmentation of bones from CT and MRI using fast level sets. In: Progress in Biomedical Optics and Imaging - SPIE (2008)
  13. Cervinka, T., Sievanen, H., Hannula, M., Hyttinen, J.: Statistical pre-processing method for peripheral quantitative computed tomography images. In: Bamidis, P.D., Pallikarakis, N. (eds.) XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. IFMBE Proceedings, vol. 29, pp. 212鈥?15. Springer, Heidelberg (2010) <a class="external" href="http://dx.doi.org/10.1007/978-3-642-13039-7_53" target="_blank" title="It opens in new window">CrossRefa>
  14. Schauerte, B., Fink, G.A.: Web-based learning of naturalized color models for human-machine interaction. In: Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010, pp. 498鈥?03 (2010)
  15. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision ass="a-plus-plus">70(2), 109鈥?31 (2006) <a class="external" href="http://dx.doi.org/10.1007/s11263-006-7934-5" target="_blank" title="It opens in new window">CrossRefa>
  16. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence ass="a-plus-plus">26(9), 1124鈥?137 (2004) <a class="external" href="http://dx.doi.org/10.1109/TPAMI.2004.60" target="_blank" title="It opens in new window">CrossRefa>
  17. Yuan, J., Bae, E., Tai, X.-C.: A study on continuous max-flow and min-cut approaches. In: Proceedings of CVPR, pp. 2217鈥?224 (2010)
  18. Punithakumar, K., Yuan, J.: A Convex Max-Flow Approach to Distribution-Based Figure-Ground Separation. SIAM Journal of Imaging Sciences 5(4), 1333鈥?354
  19. Chan, T.F., et al.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics ass="a-plus-plus">66(5), 1632鈥?648 (2006) <a class="external" href="http://dx.doi.org/10.1137/040615286" target="_blank" title="It opens in new window">CrossRefa>
  20. Bresson, X., et al.: Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision ass="a-plus-plus">28(2), 151鈥?67 (2007) <a class="external" href="http://dx.doi.org/10.1007/s10851-007-0002-0" target="_blank" title="It opens in new window">CrossRefa>
  21. Rubner, Y., et al.: The Earth Mover鈥檚 Distance as a Metric for Image Retrieval. International Journal of Computer Vision ass="a-plus-plus">40(2), 99鈥?21 (2000) <a class="external" href="http://dx.doi.org/10.1023/A:1026543900054" target="_blank" title="It opens in new window">CrossRefa>
  
• 作者单位：Jose-Antonio P茅rez Carrasco (17)
  Carmen Serrano Gotarredona (17)
  Cristina Su谩rez-Mej铆as (18)
  Bego帽a Acha-Pi帽ero (17)
  
  17. Signal and Communications Department, University of Seville, Camino de los Descubrimientos, s/n., 41092, Sevilla, Spain
  18. Virgen del Rocio Hospital, Seville, Spain
  
• ISSN：1611-3349

文摘

In this paper an automatic algorithm for segmentation of bone structures in CT volumes has been developed. This is a complicated task because bones present intensities overlapping with those of surrounding tissues. This overlapping happens because of the presence of some diseases and the different densities present in the bones, providing values similar to those in other tissues like muscle, fat or some organs. In our implementation, gray information and statistical information have been combined to be used as input to a continuous max-flow algorithm to get accurate and fast bone segmentation. Twenty CT images have been automatically segmented and several coefficients such as DICE, Jaccard, Sensitivity and Positive Predictive Value (PPV) indexes have been computed. High sensitivity values above 0.97 were obtained, which shows that the results are promising. Besides, low computational times under 0.6s in the max-flow algorithm were obtained, implying lower times in comparison to many algorithms in the literature.