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Boundary Detection of Echocardiographic Images During Mitral Regurgitation

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Recent Advances in Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 804))

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Abstract

In case of significant Mitral Regurgitation (MR), left ventricle has to accommodate both the stroke volume and the regurgitant volume with each heart beat so it leads to volume overload of the left ventricle. The left ventricle dilates and becomes hyper-dynamic for compensation. The left atrial and pulmonary venous pressures increase sharply in case of acute severe MR, leading to pulmonary congestion and pulmonary edema. A gradual increase in left atrial size, by way of compliance, compensates in chronic MR, so that left atrial and pulmonary venous pressures do not increase until late in the course of the disease. An increase in after load, contractile dysfunction, and heart failure occur in case of progressive left ventricular dilation. This entails the detection of boundaries of heart’s chambers, for which two new models, viz. the Fast Region Active Contour Model (FRACM) and the Novel Selective Binary and Gaussian Filtering Regularized Level Set (NSBGFRLS) have been developed and presented in the chapter. The proposed models the FRACM and the NSBGFRLS are the much faster algorithms than the existing algorithms to detect the boundaries of the heart chambers. The performance of these two boundary detection models has been experimented and the results are tabulated, plotted and compared with the performance of other existing models which are also employed for boundary detection of echocardiographic images. The performance of the proposed models is superior as compared to other existing models. This has been demonstrated to the clinicians at PGIMER, Chandigarh, India.

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References

  1. Lohitha, R.V., Zaheeruddin, S.: Active contours with new signed pressure force function for echocardiographic image segmentation. Int. J. Innov. Technol. Res. 4(5), 3674–3678 (2016)

    Google Scholar 

  2. de Alexandria, A.R., Cortez, P.C., Bessa, J.A., da Silva, Félix J.H., de Abreu, J.S., de Albuquerque, V.H.: pSnakes: a new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images. Comput. Methods Programs Biomed. 116(3), 260–273 (2014)

    Article  Google Scholar 

  3. Pedrosa, J., Queirós, S., Bernard, O., Engvall, J., Edvardsen, T., Nagel, E., Hooge, J.D.: Fast and fully automatic left ventricular segmentation and tracking in echocardiography using shape-based b-spline explicit active surfaces. IEEE Trans. Med. Imaging 36(11), 2287–2296 (2017)

    Article  Google Scholar 

  4. Saini, K., Dewal, M.L., Rohit, M.K.: A fast region-based active contour model for boundary detection of echocardiographic images. J. Digit. Imaging 25(2), 271–278 (2012). Springer

    Article  Google Scholar 

  5. Saini, K., Dewal, M.L., Rohit, M.K.: Level set based on new signed pressure force function for echocardiographic image segmentation. Int. J. Innov. Appl. Stud. 3(2), 560–569 (2013)

    Google Scholar 

  6. Terzopoulos, D.: On matching deformable models to images. In: Proceedings of Optical Society of America, Topical Meeting on Machine Vision, vol. 12, pp. 160–163 (1987)

    Google Scholar 

  7. Lui, G., Li, H.: Robust evolution method of active contour models and application in segmentation of image sequence. J. Electr. Comput. Eng. 2018, 1–11 (2018)

    MathSciNet  Google Scholar 

  8. Terzopoulos, D., Fleischer, K.: Deformable models. Vis. Comput. 4(6), 306–331 (1988)

    Article  Google Scholar 

  9. Ma, W., Sun, S.: Deformable surface 3D reconstruction from a single image by linear programming. KSII Trans. Internet Inf. Syst. 11(6) (2017)

    Google Scholar 

  10. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005)

    Article  Google Scholar 

  11. Thomas, T., George, A., Indira, K.P.: Effective iris recognition system. In: Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016), vol. 25, pp. 464–472 (2016)

    Article  Google Scholar 

  12. Chong, E., Familiar, A.M., Shim, W.M.: Reconstructing dynamic visual objects in V1. Proc. Natl. Acad. Sci. 113(50), 1453–1458 (2016)

    Article  Google Scholar 

  13. Outomuro, D., Johansson, F.: A potential pitfall in studies of biological shape: does size matter? J. Anim. Ecol. 86, 1447–1457 (2017)

    Article  Google Scholar 

  14. Davatzikos, C.: Computational neuroanatomy using brain deformations: from brain parcellation to multivariate pattern analysis and machine learning. Med. Image Anal. 33, 149–154 (2016)

    Article  Google Scholar 

  15. Wang, J., Zhao, S., Liu, Z., Tian, Y., Duan, F., Pan, Y.: An active contour model based on adaptive threshold for extraction of cerebral vascular structures. Comput. Math. Methods Med. 1–10 (2016)

    MATH  Google Scholar 

  16. Kumar, P., Lewis, P., McCarthy, T.: The potential of active contour models in extracting road edges from mobile laser scanning data. Infrastructures 2(9), 1–16 (2017)

    Google Scholar 

  17. Reynolds, S., Abrahamsson, T., Schuck, R., Sjöström, P.J., Schultz, S.R., Dragotti, P.L.: ABLE: an activity-based level set segmentation algorithm for two-photon calcium imaging data. ENeuro 4(5), 12–17 (2017)

    Article  Google Scholar 

  18. Rangarajan, V., Chacko, J., Romano, S., Jue, J., Jariwala1, N., Chung, J., Farzaneh, A.: Left ventricular long axis function assessed during cine-cardiovascular magnetic resonance is an independent predictor of adverse cardiac events. J. Cardiovasc. Magn. Reson. 18(15), 1–10 (2016)

    Google Scholar 

  19. Liu, G., Li, H., Yang, L.: A topology preserving method of evolving contours based on sparsity constraint for object segmentation. IEEE Access 5, 19971–19982 (2017)

    Article  Google Scholar 

  20. Rifai, H., Bloch, I., Hutchinson, S., Wiart, J., Garnero, L.: Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account. Med. Image Anal. 4(3), 219–233 (2000)

    Article  Google Scholar 

  21. Ohyama, W., Wakabayashi, T., Kimura, F., Tsuruoka, S., Sekioka, K.: Automatic left ventricular endocardium detection in echocardiograms based on ternary thresholding method. In: Proceedings of IEEE 15th International Conference on Pattern Recognition, Barcelona, Spain, Aug 2000

    Google Scholar 

  22. Hansegår, J., Steen, E., Rabben, S.I., Torp, A.H., Frigstad, S., Olstad, B.: Knowledge based extraction of the left ventricular endocardial boundary from 2D echocardiograms. In: Proceedings of IEEE Ultrasonics Symposium (2004)

    Google Scholar 

  23. Valverde, F.L., Guil, N., Muñoz, J.: Segmentation of vessels from mammograms using a deformable model. Comput. Methods Programs Biomed. 73(3), 233–247 (2004)

    Article  Google Scholar 

  24. Chang, H.H., Valentino, D.J.: An electrostatic deformable model for medical image segmentation. Comput. Med. Imaging Graph. 32(1), 22–35 (2008)

    Article  Google Scholar 

  25. Zhu, S., Bu, X., Zhou, Q.: A novel edge preserving active contour model using guided filter and harmonic surface function for infrared image segmentation. IEEE Access 6, 5493–5510 (2018)

    Article  Google Scholar 

  26. Mostaco-Guidolin, L., Hajimohammadi, S., Vasilescu, D.M., Hackett, T.L.: Application of Euclidean distance mapping for assessment of basement membrane thickness distribution in asthma. J. Appl. Physiol. 123(2), 473–481 (2017)

    Article  Google Scholar 

  27. Zampiroli, F., Filipe, L.: A fast CUDA-based implementation for the Euclidean distance transform. In: Proceedings of International Conference on High Performance Computing & Simulation (HPCS) (2017)

    Google Scholar 

  28. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  Google Scholar 

  29. Pradhan, S., Patra, D.: Unsupervised brain magnetic resonance image segmentation using HMRF-FCM framework. In: Proceedings of IEEE Annual India Conference (INDICON) (2009)

    Google Scholar 

  30. Coeurjolly, D., Foare, M., Gueth, P., Lachaud, J.O.: Piecewise smooth reconstruction of normal vector field on digital data. Comput. Graph. Forum 35(7), 1–11 (2016)

    Article  Google Scholar 

  31. Zhang, K., Zhang, L., Song, H., Zhou, W.: Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis. Comput. 28(4), 668–676 (2010)

    Article  Google Scholar 

  32. Xu, J., Janowczyk, A., Chandran, S., Madabhushi, A.: A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation. In: Proceedings of SPIE 7623, Medical Imaging 2010: Image Processing, 76230Y (2010)

    Google Scholar 

  33. Du, W., Chen, N., Liu, D.: Topology adaptive water boundary extraction based on a modified balloon snake: using GF-1 satellite images as an example. Remote Sens. 9(2), 1–25 (2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Galgotias College of Engineering and Technology, Greater Noida, 201306, UP, India

    K. Chauhan & R. K. Chauhan

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  1. K. Chauhan
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  2. R. K. Chauhan
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Correspondence to K. Chauhan .

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Editors and Affiliations

  1. Department of Computer Science, South Valley University, Luxor, Egypt

    Mahmoud Hassaballah

  2. Department of Information Technology, Zagazig University, Zagazig, Egypt

    Khalid M. Hosny

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Chauhan, K., Chauhan, R.K. (2019). Boundary Detection of Echocardiographic Images During Mitral Regurgitation. In: Hassaballah, M., Hosny, K. (eds) Recent Advances in Computer Vision. Studies in Computational Intelligence, vol 804. Springer, Cham. https://doi.org/10.1007/978-3-030-03000-1_12

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