Evolution Model Based on Prior Information for Narrow Joint Segmentation

doi:10.1007/s40305-019-00265-w

Journal of the Operations Research Society of China ›› 2019, Vol. 7 ›› Issue (4): 629-642.doi: 10.1007/s40305-019-00265-w

所属专题： Continuous Optimization

收稿日期:2019-05-08 修回日期:2019-06-05 出版日期:2019-11-30 发布日期:2019-11-28
通讯作者: Jing Qin, Xin Wang, Shuai Xu, Zhen Ye, Chao-Zheng Zhou E-mail:jing.qin@uky.edu;xinwang@shu.edu.cn;xushuai.93@163.com;yezhen@shanghai-electric.com;zhouchzh3@shanghai-electric.com

Evolution Model Based on Prior Information for Narrow Joint Segmentation

Xin Wang¹, Shuai Xu¹, Zhen Ye², Chao-Zheng Zhou², Jing Qin³

1 Department of Mathematics, Shanghai University, Shanghai 200444, China;
2 Shanghai Electric Central Research Institute, Shanghai 200070, China;
3 Department of Mathematics, University of Kentucky, Lexington, KY 40506-0027, USA

Received:2019-05-08 Revised:2019-06-05 Online:2019-11-30 Published:2019-11-28
Contact: Jing Qin, Xin Wang, Shuai Xu, Zhen Ye, Chao-Zheng Zhou E-mail:jing.qin@uky.edu;xinwang@shu.edu.cn;xushuai.93@163.com;yezhen@shanghai-electric.com;zhouchzh3@shanghai-electric.com
Supported by:
This research was supported in part by the National Natural Science Foundation of China (Nos. 11771276, 11471208) and the capacity construction project of local universities in Shanghai (No. 18010500600). The research of Jing Qin was supported by the National Science Foundation of USA (No. DMS-1941197).

摘要/Abstract

Abstract: Automated segmentation of hip joint computed tomography images is significantly important in the diagnosis and treatment of hip joint disease. In this paper, we propose an automatic hip joint segmentation method based on a variational model guided by prior information. In particular, we obtain prior features by automatic sample selection, get a discriminative function by training these selected samples and then integrate this prior information into our variational model. Numerical results demonstrate that the proposed method has high accuracy in segmenting narrow joint regions.

Key words: Image segmentation, Hip joint, CT, Feature prior, Level set, Variational model

中图分类号:

65K10
94A08

. [J]. Journal of the Operations Research Society of China, 2019, 7(4): 629-642.

Xin Wang, Shuai Xu, Zhen Ye, Chao-Zheng Zhou, Jing Qin. Evolution Model Based on Prior Information for Narrow Joint Segmentation[J]. Journal of the Operations Research Society of China, 2019, 7(4): 629-642.

参考文献

[1] Zoroofi, A.R., et al.:Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans. Inform. Technol. B 7(4), 329-334(2004)
[2] Prasoon, A., Petersen, K., Igel, C., et al.:Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network. MICCAI 246-253(2013)
[3] Chu, C., Chen, C., Liu, L., et al.:FACTS:fully automatic CT segmentation of a hip joint. Ann. Biomed. Eng. 43(5), 1247-1259(2015)
[4] Chen, F., Liu, J., Zhao, Z., et al.:3D feature-enhanced network for automatic femur segmentation. IEEE J. Biomed. Health 23, 1-1(2017)
[5] Du, M., Ding, Y., Jia, Q.:A multi-threshold segmentation method based on ant colony algorithm. ICMV 878402-878409(2013)
[6] Xiao, B., Jing, Y., Guan, Y., et al.:A novel automatic thresholding segmentation method with local adaptive thresholds. arXiv preprint, arXiv:1305.5160
[7] Cheng, Y., Zhou, S., Wang, Y., et al.:Automatic segmentation technique for acetabulum and femoral head in CT images. Pattern Recognit. 46(11), 2969-2984(2013)
[8] Adams, R., Bischof, L.:Seeded region growing. IEEE Trans. Pattern Anal. 16(6), 641-647(1994)
[9] Lorensen, W.E., Cline, H.E.:Marching cubes:a high resolution 3D surface construction algorithm. Comput. Graph 21(4), 163-169(1987)
[10] Neill, G.T., Lee, W., Beaule, P.E.:Segmentation of cam-type femurs from CT scans. Visual Comput. 28(2), 205-218(2012)
[11] Xu, C., Prince, J.L.:Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359-369(1998)
[12] Ronneberger, O., Fischer, P., Brox, T., et al.:U-Net:Convolutional Networks for Biomedical Image Segmentation. MICCAI 234-241(2015)
[13] Zheng, S., Jayasumana, S., Romeraparedes, B., et al.:Conditional random fields as recurrent neural networks. ICCV 1529-1537(2015)
[14] Lu, F., Wu, F., Hu, P., et al.:Automatic 3D liver location and segmentation via convolutional neural network and graph cut. CARS 12(2), 171-182(2017)
[15] Ghosh, M., Berger, J.:Statistical decision theory and Bayesian analysis. J. Am. Stat. Assoc. 83(401), 266-266(1988)
[16] Huang, J., Griffith, J.F., Wang, D., et al.:Graph-cut-based segmentation of proximal femur from computed tomography images with shape prior. J. Med. Biol. Eng. 35(5), 594-607(2015)
[17] Chu, C., Bai, J., Wu, X., et al.:MASCG:multi-atlas segmentation constrained graph method for accurate segmentation of hip CT images. Med Image Anal. 26(1), 173-184(2015)
[18] Caselles, V., Kimmel, R., Sapiro, G., et al.:Geodesic active contours. ICCV 22(1), 61-79(1995)
[19] Chan, T.F., Vese, L.A.:Active contours without edges. IEEE Trans. Image Process. 10(2), 266-277(2001)
[20] Li, C., Kao, C., Gore, J.C., et al.:Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940-1949(2008)
[21] Wang, L., He, L., Mishra, A., et al.:Active contours driven by local Gaussian distribution fitting energy. Signal Process. 89(12), 2435-2447(2009)
[22] Peng, Y., Pi, L., Shen, C.:A semi-automatic method for burn scar delineation using a modified Chan-Vese model. Comput. Geosci. 35(2), 183-190(2009)
[23] Peng,Y.,Bao,L.,Pi,L.:Object(s)-of-interestsegmentationforimageswithinhomogeneousintensities based on curve evolution. Neurocomputing 195(3), 13-18(2016)
[24] Zhu, S., Yuille, A.L.:Region competition:unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. 18(9), 884-900(1996)
[25] Kim, J., Fisher, J.W., Yezzi, A.J., et al.:A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. Image Process. 14(10), 1486-1502(2005)
[26] Rosenhahn, B., Brox, T., Weickert, J., et al.:Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int. J. Comput. Vis. 73(3), 243-262(2007)
[27] Shi, Y., Chen, Z., Qi, Z., et al.:A novel clustering-based image segmentation via density peaks algorithm with mid-level feature. Neural Comput. Appl. 28(1), 29-39(2017)
[28] Lim, J.J., Zitnick, C.L., Dollar, P., et al.:Sketch tokens:a learned mid-level representation for contour and object detection. CVPR 3158-3165(2013)
[29] Ojala, T., Pietikainen, M., Maenpaa, T.:Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. 24(7), 971-987(2002)
[30] Li, C., Xu, C., Gui, C., et al.:Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243-3254(2010)

Evolution Model Based on Prior Information for Narrow Joint Segmentation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价