Abstract
Prostate segmentation from extracorporeal ultrasound (ECUS) images is considerably challenging due to the prevailing speckle noise, shadow artifacts, and low contrast intensities. In this paper, we proposed a cascaded shape regression (CSR) method for automatic detection and localization of the prostate. A sequence of random fern predictors are trained in a boosted regression manner. Shape-indexed features are used to achieve invariance against geometric scales, translation, and rotation of prostate shapes. The boundary detected by CSR is used as the initialization for accurate segmentation by using a dynamic directional gradient vector flow (DDGVF) snake model. DDGVF proves to be useful to distinguish desired edges from false edges in ECUS images. The proposed method is tested on both longitudinal- and axial- view ECUS images and achieves Root Mean Square Error (RMSE) under 1.98 mm (=4.95 pixels). It outperforms the active appearance model in terms of RMSE, failure rate, and area error metrics. The testing time of CSR+DDGVF is less than 1 second per image.
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References
Ghose, S., Oliver, A., Marti, R., Llado, X., Vilanova, J.C., Freixenet, J., Mitra, J., Sidibé, D., Meriaudeau, F.: A Survey of Prostate Segmentation Methodologies in Ultrasound, Magnetic Resonance and Computed Tomography Images. Comput. Methods Programs Biomed. 108(1), 262–287 (2012)
Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR, pp. 1078–1085 (2010)
Cao, X., Wei, Y., Wen, F., Sun, J.: Face Alignment by Explicit Shape Regression. In: CVPR (2012)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. Pattern Analysis and Machine Intelligence 32(3), 448–461 (2010)
Zaim, A., Jankun, J.: An Energy-Based Segmentation of Prostate from Ultrasound Images Using Dot-Pattern Select Cells. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 297–300 (2007)
Ladak, H.M., Mao, F., Wang, Y., Downey, D.B., Steinman, D.A., Fenster, A.: Prostate Segmentation from 2D Ultrasound Images. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3188–3191 (2000)
Gong, L., Pathak, S.D., Haynor, D.R., Cho, P.S., Kim, Y.: Parametric Shape Modeling Using Deformable Superellipses for Prostate Segmentation. IEEE Transactions on Medical Imaging 23, 340–349 (2004)
Badiei, S., Salcudean, S.E., Varah, J., Morris, W.J.: Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fitting. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 17–24. Springer, Heidelberg (2006)
Fan, S., Voon, L.K., Sing, N.W.: 3D Prostate Surface Detection from Ultrasound Images Based on Level Set Method. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part II. LNCS, vol. 2489, pp. 389–396. Springer, Heidelberg (2002)
Zouqi, M., Samarabandu, J.: Prostate Segmentation from 2D Ultrasound Images Using Graph Cuts and Domain Knowledge. In: Canadian Conference on Computer and Robot Vision, pp. 359–362. IEEE Computer Society Press, USA (2008)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Richard, W.D., Keen, C.G.: Automated Texture Based Segmentation of Ultrasound Images of the Prostate. Computerized Medical Imaging and Graphics 20, 131–140 (1996)
Mohamed, S.S., Youssef, A.M., El-Saadany, E.F., Salama, M.M.A.: Prostate Tissue Characterization Using TRUS Image Spectral Features. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 589–601. Springer, Heidelberg (2006)
Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 681–685 (2001)
Zhou, S.: Shape regression machine and efficient segmentation of left ventricle endocardium from 2D B-mode echocardiogram. Medical Image Analysis 14, 563–581 (2010)
Friedman, J.H.: Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29(5), 1189–1232 (2001)
Duffy, N., Helmbold, D.P.: Boosting methods for regression. Machine Learning 47(2-3), 153–200 (2002)
Breiman, L.: Random forests. In: Machine learning (2001)
Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: CVPR (2010)
Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 278–291. Springer, Heidelberg (2012)
Open source C++ AAM implementation, http://www2.imm.dtu.dk/~aam/
Cheng, J., Foo, S.: Dynamic directional gradient vector flow for snakes. IEEE Transactions on Image Processing 15(6), 1563–1571 (2006)
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Cheng, J., Xiong, W., Gu, Y., Chia, S.C., Wang, Y. (2013). Cascaded Shape Regression for Automatic Prostate Segmentation from Extracorporeal Ultrasound Images. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds) Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. MIAR AE-CAI 2013 2013. Lecture Notes in Computer Science, vol 8090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40843-4_8
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DOI: https://doi.org/10.1007/978-3-642-40843-4_8
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