Abstract
In this paper, we modified GrabCut for gray-scale slice-stacked medical image segmentation. First, GrabCut was extended from planar to volume image processing. Second, we simplified manual interaction by drawing a polygon for one volume instead of a rectangle. After that, twenty human brain computerized tomography images were analyzed. Experimental results show that the modified algorithm is simple and fast, and enhances segmentation accuracy compared with the confidence connection algorithm. Moreover, the algorithm is reproducible with respect to different users and consequently it can release physicians from this kind of time-consuming and laborious tasks. In addition, this method can be used for other types of medical volume image segmentation.
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References
Dhawan, A.P.: Medical Image Analysis. Wiley, New York (2011)
Ezzell, G.A., Galvin, J.M., et al.: Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee. Med. Phys. 30(8), 2089–2115 (2003)
Xing, L., Thorndyke, B., et al.: Overview of image-guided radiation therapy. Med. Dosim. 31(2), 91–112 (2006)
Xie, Y., Djajaputra, D., et al.: Intrafractional motion of the prostate during hypofractionated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 72(1), 236–246 (2008)
Xie, Y., Chao, M., et al.: Feature-based rectal contour propagation from planning CT to cone beam CT. Med. Phys. 35(10), 4450–4459 (2008)
Chao, M., Xie, Y., Xing, L.: Auto-propagation of contours for adaptive prostate radiation therapy. Phys. Med. Biol. 53(17), 4533 (2008)
Khodr, Z.G., Sak, M.A., et al.: Determinants of the reliability of ultrasound tomography sound speed estimates as a surrogate for volumetric breast density. Med. Phys. 42(10), 5671–5678 (2015)
Zhou, W., Xie, Y.: Interactive contour delineation and refinement in treatment planning of image-guided radiation therapy. J. Appl. Clin. Med. Phys. 15(1), 4499 (2014)
Zhou, W., Xie, Y.: Interactive medical image segmentation using snake and multiscale curve editing. Comput. Math. Methods Med. 2013 (2013)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 322–325 (2000)
Dirami, A., Hammouche, K., et al.: Fast multilevel thresholding for image segmentation through a multiphase level set method. Sig. Process. 93(1), 139–153 (2013)
Cai, H., Yang, Z., et al.: A new iterative triclass thresholding technique in image segmentation. IEEE Trans. Image Process. 23(3), 1038–1046 (2014)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 1(11), 1222–1239 (2001)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. ICCV 2001(1), 105–112 (2001)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309–314 (2004)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Yin, S., Zhao, X., Wang, W., Gong, M.: Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization. Pattern Recogn. 47(9), 2894–2907 (2014)
Temoche, P., Carmona, R.: A volume segmentation approach based on GrabCut. CLEI Electron. J. 16(2), 4–4 (2013)
Park, S., Yoo, J.H.: Human segmentation based on GrabCut in real-time video sequences. ICCE 2014, 111–112 (2014)
Gao, Z., Shi, P., et al.: A mutual GrabCut method to solve co-segmentation. EURASIP J. Image Video Process. 2013(1), 1–11 (2013)
Hernandez-Vela, A., Reyes, M., et al.: Grabcut-based human segmentation in video sequences. Sensors 12(11), 15376–15393 (2012)
Li, J.G., Li, X.N., et al.: Application of GrabCut in human serially sectioned image segmentation. Comput. Technol. Develop. 21(12), 246–249 (2011)
Meyer, G.P., Do, M.N.: 3D GrabCut: interactive foreground extraction for reconstructed 3D scenes. In: Eurographics Workshop on 3D Object Retrieval 2015, pp. 1–6. Eurographics Association (2015)
Ramirez, J., Temoche, P., Carmona, R.: A volume segmentation approach based on GrabCut. CLEI Electron. J. 16(2), 4–4 (2013)
Piekos, T.: Confidence connected segmentation using ITK. Insight J. 2007 (2007)
Mortensen, E.N., Barrett, W.A.: Intelligent scissors for image composition. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 191–198. ACM (1995)
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive image segmentation using an adaptive GMMRF model. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24670-1_33
Acknowledgment
This work is supported by grants from National Natural Science Foundation of China (Grant No. 81501463), Guangdong Innovative Research Team Program (Grant No. 2011S013), National 863 Programs of China (Grant No. 2015AA043203), Shenzhen Fundamental Research Program (Grant Nos. JCYJ20140417113430726, JCYJ20140417113430665 and JCYJ201500731154850923) and Beijing Center for Mathematics and Information Interdisciplinary Sciences.
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Ji, Z., Yu, S., Wu, S., Xie, Y., Yang, F. (2016). Improved GrabCut for Human Brain Computerized Tomography Image Segmentation. In: Yin, X., Geller, J., Li, Y., Zhou, R., Wang, H., Zhang, Y. (eds) Health Information Science. HIS 2016. Lecture Notes in Computer Science(), vol 10038. Springer, Cham. https://doi.org/10.1007/978-3-319-48335-1_3
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DOI: https://doi.org/10.1007/978-3-319-48335-1_3
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