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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

3D Shared Matting Method for Directly Extracting Standard Organ Models from Human Body Color Volume Image

Author(s): Bin Liu, Xiaolei Niu, Xiaohui Zhang, Song Zhang, Jianxin Zhang, Wen Qi and Liang Yang*

Volume 16, Issue 9, 2020

Page: [1170 - 1181] Pages: 12

DOI: 10.2174/1573405616666200103100030

Price: $65

Abstract

Background: In some medical applications (e.g., virtual surgery), standard human organ models are very important and useful. Now that real human body slice image sets have been collected by several countries, it is possible to obtain real standard organ models.

Introduction: Understanding how to abandon the traditional model construction method of Photoshop sketching slice by slice and directly extracting 3D models from volume images has been an interesting and challenging issue. In this paper, a 3D color volume image matting method has been proposed to segment human body organ models.

Methods: First, the scope of the known area will be expanded by means of propagation. Next, neighborhood sampling to find the best sampling for voxels in an unknown region will be performed and then the preliminary opacity using the sampling results will be calculated.

Results: The final result will be obtained by applying local smoothing to the image.

Conclusion: From the experimental results, it has been observed that our method is effective for real standard organ model extraction.

Keywords: Human body slice, color volume image, image matting, 3D models, opacity, visualization.

Graphical Abstract

[1]
Liu K, Fang B, Wu Y, et al. Anatomical education and surgical simulation based on the Chinese Visible Human: a three-dimensional virtual model of the larynx region. Anat Sci Int 2013; 88(4): 254-8.
[http://dx.doi.org/10.1007/s12565-013-0186-x] [PMID: 23801001]
[2]
Shin DS, Chung MS, Park JS, et al. Three-dimensional surface models of detailed lumbosacral structures reconstructed from the Visible Korean. Ann Anat 2011; 193(1): 64-70.
[http://dx.doi.org/10.1016/j.aanat.2010.09.001] [PMID: 20951015]
[3]
Xu H, Zhang X, Christe A, et al. Anatomic pathways of peripancreatic fluid draining to mediastinum in recurrent acute pancreatitis: visible human project and CT study. PLoS One 2013; 8(4)e62025
[http://dx.doi.org/10.1371/journal.pone.0062025] [PMID: 23614005]
[4]
Li T, Wang P, Qiu L, Fang X, Shang Y. Optimize Illumination Parameter of Low-Level Laser Therapy for Hemorrhagic Stroke by Monte Carlo Simulation on Visible Human Dataset. IEEE Photonics J 2018; 10(3): 1-9.
[http://dx.doi.org/10.1109/JPHOT.2018.2842467]
[5]
Li T, Zhao Y, Sun Y, Li K. Effects of wavelength, beam type and size on cerebral low-level laser therapy by a Monte Carlo study on visible Chinese human. J Innov Opt Health Sci 2015; 8(01)1540002
[http://dx.doi.org/10.1142/S1793545815400027]
[6]
Yuan Y, Qi L, Luo S. The reconstruction and application of virtual Chinese human female. Comput Methods Programs Biomed 2008; 92(3): 249-56.
[http://dx.doi.org/10.1016/j.cmpb.2008.05.011] [PMID: 18644313]
[7]
Dai JX, Chung MS, Qu RM, Yuan L, Liu SW, Shin DS. The Visible Human Projects in Korea and China with improved images and diverse applications. Surg Radiol Anat 2012; 34(6): 527-34.
[http://dx.doi.org/10.1007/s00276-012-0945-8] [PMID: 22402591]
[8]
Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 2008; 30(2): 228-42.
[http://dx.doi.org/10.1109/TPAMI.2007.1177] [PMID: 18084055]
[9]
Gastal ESL, Oliveira MM. Shared sampling for real‐time alpha matting. Proceedings of Eurographics. 2010 May 4-7; Norrköping, Sweden. 575-84.
[10]
He B, Wang G, Zhang C. Iterative transductive learning for automatic image segmentation and matting with RGB-D data. J Vis Commun Image Represent 2014; 25(5): 1031-43.
[http://dx.doi.org/10.1016/j.jvcir.2014.03.002]
[11]
Cho D, Kim S, Tai YW. Consistent matting for light field images. European Conference on Computer Vision. 2014 Sep 6-12; Zurich, Switzerland. 90-104.
[12]
Fiss J, Curless B, Szeliski R. Light field layer matting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015 Jun 8-10; Boston, Massachusetts. 623-31.
[13]
Donghyeon Cho , Sunyeong Kim , Yu-Wing Tai , Kweon SI. In So Kweon. Automatic trimap generation and consistent matting for light-field images. IEEE Trans Pattern Anal Mach Intell 2017; 39(8): 1504-17..
[http://dx.doi.org/10.1109/TPAMI.2016.2606397] [PMID: 28113357]
[14]
Feng X, Liang X, Zhang Z. A cluster sampling method for image matting via sparse coding. European Conference on Computer Vision. 2016 Oct 8-16; Amsterdam, The Netherlands. 204-19.
[http://dx.doi.org/10.1007/978-3-319-46475-6_13]
[15]
Johnson J, Varnousfaderani ES, Cholakkal H, Rajan D. Sparse coding for alpha matting. IEEE Trans Image Process 2016; 25(7): 3032-43.
[http://dx.doi.org/10.1109/TIP.2016.2555705] [PMID: 28113175]
[16]
Karacan L, Erdem A, Erdem E. Image matting with KL-divergence based sparse sampling. Proceedings of the IEEE International Conference on Computer Vision. 2015 Dec 13-16; Santiago, Chile. 424-32.
[http://dx.doi.org/10.1109/ICCV.2015.56]
[17]
Zou D, Chen X, Cao G, Wang X. Video matting via sparse and low-rank representation. Proceedings of the IEEE International Conference on Computer Vision. 2015 Dec 13-16; Santiago, Chile. 1564-72.
[http://dx.doi.org/10.1109/ICCV.2015.183]
[18]
Xu N, Price B, Cohen S, Huang T. Deep image matting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017 Jul 21-26; Honolulu, Hawaii. 2970-9.
[19]
Cho D, Tai YW, Kweon I. Natural image matting using deep convolutional neural networks. European Conference on Computer Vision. 2016 Oct 8-16; Amsterdam, The Netherlands. 626-43.
[http://dx.doi.org/10.1007/978-3-319-46475-6_39]
[20]
Shen X, Tao X, Gao H, Zhou C, Jia J. Deep automatic portrait matting. European Conference on Computer Vision. 2016 Oct 8-16; Amsterdam, The Netherlands. 92-107.
[21]
Hu H, Pang L, Shi Z. Image matting in the perception granular deep learning. Knowl Base Syst 2016; 102: 51-63.
[http://dx.doi.org/10.1016/j.knosys.2016.03.018]
[22]
Tan G, Chen H, Qi J. A novel image matting method using sparse manual clicks. Multimedia Tools Appl 2016; 75(17): 10213-25.
[http://dx.doi.org/10.1007/s11042-015-3160-x]
[23]
Jin M, Kim BK, Song WJ. Adaptive propagation-based color-sampling for alpha matting. IEEE Trans Circ Syst Vid 2014; 24(7): 1101-10.
[http://dx.doi.org/10.1109/TCSVT.2014.2302531]
[24]
Wu H, Li Y, Miao Z, et al. A new sampling algorithm for high-quality image matting. J Vis Commun Image R 2016; 38: 573-81.
[http://dx.doi.org/10.1016/j.jvcir.2016.04.008]
[25]
Zhu X, Wang P, Huang Z. Adaptive propagation matting based on transparency of image. Multimedia Tools Appl 2018; 77(15): 19089-112.
[http://dx.doi.org/10.1007/s11042-017-5357-7]
[26]
Cho HW, Cho YR, Song WJ, Kim BK. Image Matting for Automatic Target Recognition. IEEE Trans Aerosp Electron Syst 2017; 53(5): 2233-50.
[http://dx.doi.org/10.1109/TAES.2017.2690529]
[27]
Cai ZQ, Lv L, Huang H, Hu H, Liang YH. Improving sampling-based image matting with cooperative coevolution differential evolution algorithm. Soft Comput 2017; 21(15): 4417-30.
[http://dx.doi.org/10.1007/s00500-016-2250-7]
[28]
Levin A, Rav-Acha A, Lischinski D. Spectral matting. IEEE Trans Pattern Anal Mach Intell 2008; 30(10): 1699-712.
[http://dx.doi.org/10.1109/TPAMI.2008.168] [PMID: 18703825]
[29]
Yan X, Hao Z, Huang H. Alpha matting with image pixel correlation. Int J Mach Learn Cybern 2018; 9(4): 621-7.
[http://dx.doi.org/10.1007/s13042-016-0584-1]
[30]
Wang J, Cohen MF. Optimized Color Sampling for Robust Matting. 2007 IEEE Conference on Computer Vision and Pattern Recognition. 2007 Jun 18-23; Minneapolis. 2007.
[http://dx.doi.org/10.1109/CVPR.2007.383006]
[31]
Rhemann C, Rother C, Gelautz M. Improving Color Modeling for Alpha Matting. British Machine Vision Conference. 2008 Sep 1-4; Leeds, UK. 2008.

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