Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow
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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.
KeywordsThe segmentation of left ventricle Tagging magnetic resonance image Notch bandstop filtering GVF snake
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).
- 3.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):3–9Google Scholar
- 6.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 1–1Google Scholar
- 8.Kasai M (1990) Clinical application of magnetic resonance imaging (mri) in uterine disease. Nihon Sanka Fujinka Gakkai Zasshi 42(7):711–718Google Scholar
- 9.Li ZL (2011) Tagged cardiac mr image segmentation based on texture analysis. J Clin Rehabilitative Tissue Eng Res 15(9):1521–1524Google Scholar
- 14.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 67–70Google Scholar
- 20.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-2015-308773Google Scholar
- 23.Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. Acm Trans Graph 31(6):139Google Scholar
- 24.Yuwei W, Liang J, Wang Y (2010) A method for segmentation of the cardiac mr images based on ggvf snake. J Image GraphGoogle Scholar
- 25.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):1902–1901Google Scholar
- 26.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 1–8Google Scholar
- 30.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):82–88Google Scholar