Abstract
In the segmentation of cardiac tagging magnetic resonance (tMR) images, it is difficult to segment the left ventricle automatically by using the traditional segmentation model because of the interference caused by the tags. A new snake model based on hybrid gradient vector flow (HGVF) is proposed by us to improve this segmentation. Due to the different characteristics between endocardium and epicardium of the left ventricle (LV), several gradient vector flows (GVFs) with distinctive boundary information would be fused to segment these two sub regions individually. For segmentation of endocardium, we construct a new HGVF in snake model fused by three independent GVFs. These flows are respectively exported from the original cardiac tMR image, the tags-removed image and the local-filtered image. On the other hand, since the epicardium is with a nearly-circle shape, we construct the other HGVF which is composed of two different GVFs. One of them is derived from the tags-removed image either and the other one is derived from the ideal circle-shape image. Some experiments have been done to validate our new segmentation model. The average overlap of the endocardium segmentation is 89.67% (its mean absolute distance is 1.86 pixels), and the average overlap of the epicardium segmentation is 95.88% (its mean absolute distance is 1.64 pixels). Experimental results show that the proposed method improves the segmentation performance compared to some available methods effectively.
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References
Avendi MR, Kheradvar A, Jafarkhani H (2015) A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri. Med Image Anal 30:108–119
Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209
Chen M, Ma Y, Li Y, Wu D, Zhang Y (2017) Wearable 2.0: enabling Human-Cloud integration in next generation healthcare systems. IEEE Commun Mag 54(12):3–9
Dakua SP, Sahambi JS (2010) A strategic approach for cardiac mr left ventricle segmentation. Cardiovasc Eng 10(3):163
Dharanibai G, Raina JP (2014) Automatic segmentation of left ventricle endocardium from cardiac mr images using active contours driven by local and global intensity fitting energy. Int J Med Eng Inform 6(2):115–134
Hajiaghayi M, Groves E, Jafarkhani H, Kheradvar A (2016) A 3d active contour method for automated segmentation of the left ventricle from magnetic resonance images. IEEE Trans Biomed Eng, pp 1–1
Huang S, Liu J, Lee LC, Venkatesh SK, Teo LL, Au C et al (2011) An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine mr images. J Digit Imaging 24(4):598–608
Kasai M (1990) Clinical application of magnetic resonance imaging (mri) in uterine disease. Nihon Sanka Fujinka Gakkai Zasshi 42(7):711–718
Li ZL (2011) Tagged cardiac mr image segmentation based on texture analysis. J Clin Rehabilitative Tissue Eng Res 15(9):1521–1524
Liu FC, Chen Q, Sun QS, Ann HP, Xia DS (2009) Texture classification and shape statistics variational approach for segmentation of left ventricle tagged mr images. J Softw 20(1):30–40
Liu L, Wu Y, Wang Y (2009) A novel method for segmentation of the cardiac mr images using generalized ddgvf snake models with shape priors. Inf Technol J 8 (4):486–494
Liu L, Song J, Chen M, Liu B (2012) Segmentation of the left ventricle from cardiac mr images using active contours with negvf. J Bioinforma Intell Control 1(1):56–63
Liu Y, Captur G, Moon JC, Guo S, Yang X, Zhang S, et al. (2015) Distance regularized two level sets for segmentation of left and right ventricles from cine-mri. Magn Reson Imaging 34(5):699–706
Makram AW, Khalifa AM, El-Wakad MT, El-Rewaidy H (2014) Evaluation of cardiac left ventricular mass from tagged magnetic resonance imaging. Biomed Eng Conf, pp 67–70
Ngo TA, Lu Z, Carneiro G (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 35:159–171
Pham VT, Tran TT (2016) Active contour model and nonlinear shape priors with application to left ventricle segmentation in cardiac mr images. Optik - Int J Light Electron Opt 127(3):991–1002
Pham VT, Tran TT, Shyu KK, Lin LY, Wang YH, Lo MT (2014) Multiphase b-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac mr images. Mach Vis Appl 25 (8):1967–1987
Qin X, Tian Y, Yan P (2015) Feature competition and partial sparse shape modeling for cardiac image sequences segmentation. Neurocomputing 149(PB):904–913
Rm FIV (2014) Automatic cardiac lv segmentation in mri using modified graph cuts with smoothness and interslice constraints. Magn Reson Med 72(6):1775–1784
Varghese T, Schultz WM, Mccue AA, Lambert CT, Sandesara PB, Eapen DJ et al (2016) Physical activity in the prevention of coronary heart disease: implications for the clinician. Heart, heartjnl-2015-308773
Wang B, Gu X, Fan C, Xie H, Zhang S, Tian X et al (2015) Sparse group composition for robust left ventricular epicardium segmentation. Comput Med Imaging Graph 46:56–63
Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process A Publ IEEE Signal Process Soc 7(3):359
Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. Acm Trans Graph 31(6):139
Yuwei W, Liang J, Wang Y (2010) A method for segmentation of the cardiac mr images based on ggvf snake. J Image Graph
Zhang N, Xue-Fei YU, Guang-Wen LU (2012) Endocardium and epicardium segmentation of left ventricle in cardiac magnetic resonance images based on directional snake model. J Comput Appl 32(7):1902–1901
Zhang Y, Qiu M, Tsai CW, Hassan MM, Alamri A (2015) Health-cps: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J, pp 1–8
Zhang Y, Zhang D, Hassan MM, Alamri A, Peng L (2015) Cadre: cloud-assisted drug recommendation service for online pharmacies. Mob Netw Appl 20(3):348–355
Zhang Y, Chen M, Huang D, Wu D, Li Y (2016) Idoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Futur Gener Comput Syst 66:30–35
Zhu Y (2010) Segmentation of the left ventricle from cardiac mr images using a subject-specific dynamical model. IEEE Trans Med Imaging 29(3):669–687
Zhu M, Zhang W, Qu Q, Li M, Gao L (2015) A segmentation method of left ventricle in cardiac magnetic resonance images based on improved snake model. Sichuan Daxue Xuebao 47(2):82–88
Acknowledgements
We would like to thank Tongji Hospital, affiliated with Huazhong University of Science and Technology, for providing the experimental datasets used in this study and for offering useful medical suggestions.
This research was funded by the National Natural Science Foundation of China (Grant No. 61602519); Ministry of Education of China (MOE) Project of Humanities and Social Sciences (Project No. 16YJC860026); China Postdoctoral Science Foundation (Grant No. 2013M542021); China Postdoctoral Science Foundation (Grant No.2014M562026); the Natural Science Foundation of Hubei Province, China (Grant No. 2013CFC090); the Fundamental Research Funds for the Central Universities, Zhongnan University of Economics and Law (Grant No. 2012096).
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Yu, Z., Wang, Q., Xiong, W. et al. Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow. Multimed Tools Appl 77, 21879–21904 (2018). https://doi.org/10.1007/s11042-017-5013-2
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DOI: https://doi.org/10.1007/s11042-017-5013-2