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Ensemble Approach for Left Ventricle Segmentation

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

This paper describes a novel algorithm for segmenting the Left Ventricle (LV) from cardiac MRI. Cardiac Magnetic Resonance Imaging (CMRI) is a non invasive medical imaging technique used in radiology to form a picture of the heart. The importance of precisely identifying the LV region in a cardiac MRI is crucial as it is the key in measuring LV volumes and determining cardiac function. Hence this paper describes a new algorithm based on an improved fuzzy c means clustering. To increase the segmentation accuracy min max ant system algorithm was used to initialize the cluster centers and principal components analysis algorithm used for time optimization. The rationale is to test fuzzy c means with ant colony optimization, a studied algorithm, in the task of LV segmentation. However, till now fuzzy c means with ant colony optimization was tested in radiology problems where small amount of data needed segmentation, contrary to this case in which doctors evaluate heart condition based on many cardiac MRI. Therefore this paper demonstrate how to use principal components analysis algorithm to tackle the problem of time performance. Algorithm was evaluated on real cardiac MRI with different heart conditions dataset. Results show that this approach improves the segmentation quality compared to other methods getting the highest BDP score (fraction of the correctly classified pixels) while reserving efficiency and accuracy.

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Notes

  1. 1.

    MICCAI Workshop – Cardiac MR Left Ventricle Segmentation Challenge 2009, http://smial.sri.utoronto.ca/LV_Challenge/Home.html.

  2. 2.

    Eight segmentations on MICCAI 2009 LV Grand Challenge, https://github.com/frederiquefrouin/Medieval.

References

  1. Caroline, P., Jean-Nicloas, D.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2010)

    Google Scholar 

  2. Kang, D., Woo, J., Slomka, P.J., Dey, D., Germano, G., Jay-Kuo, C.C.: Heart chambers and whole heart segmentation techniques: review. SPIE J. Electron. Imaging 21(1), 010901 (2012)

    Article  Google Scholar 

  3. Weng, J., Singh, A., Chiu, M.Y.: Learning-based ventricle detection from cardiac MR and CT images. IEEE Trans. Med. Imaging 16(4), 378–391 (1997)

    Article  Google Scholar 

  4. Jolly, M.-P.: Automatic segmentation of the left ventricle in cardiac MR and CT images. Int. J. Comput. Vis. 70(2), 151–163 (2006)

    Article  Google Scholar 

  5. Lorenzo-Valdes, M., Sanchez-Ortiz, G., Elkington, A., Mohiaddin, R., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas ant the EM algorithm. Med. Image Anal. 8(3), 255–265 (2004)

    Article  Google Scholar 

  6. Kedenburg, G., Cocosco, C., Kothe, U., Niessen, W., Vonken, E., Viergever, M.: Automatic cardiac MRI myocardium segmentation using graphcut. In: Medical Imaging 2006, vol. 6144. SPIE (2006)

    Google Scholar 

  7. Yang, X., Yeo, S.Y., Lim, C., Su, Y., Wan, M., Zhong, L., Tan, R.S.: A framework for auto-segmentation of left ventricle from magnetic resonance images. In: APCOM & ISCM, December 2013, Singapore (2013)

    Google Scholar 

  8. Cootes, T., Cooper, D., Taylor, C., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  9. Pargios, N., et al.: Active shape models & segmentation of the left ventricle in echocardiography (2004)

    Google Scholar 

  10. Van Assen, H.C., et al.: 3D active shape model matching for left ventricle segmentation in cardiac CT (2002)

    Google Scholar 

  11. Raghtate, G.S., Salankar, S.S.: Brain tumor segmentation using fuzzy C means with ant colony optimization algorithm. Trends Technol. Sci. (2015)

    Google Scholar 

  12. Boudra, A.-E.-O.: Automated detection of the left ventricular region in magnetic resonance images by fuzzy C-means model. Int. J. Card. Imaging 13, 347–355 (1997)

    Article  Google Scholar 

  13. Mary, C.I., Raja, S.V.K.: Improved fuzzy C-means clusters with ant colony optimization. Int. J. Comput. Sci. Emerg. Technol. 1(4), 1–6 (2010)

    Google Scholar 

  14. Feng, Y., Wang, Z.: Ant colony optimization for image segmentation, ant colony optimization – methods and application (2011)

    Google Scholar 

  15. Malisia, A.R., Tizhoosh, H.R.: Applying ant colony optimization to binary thresholding. IEEE (2006)

    Google Scholar 

  16. Ouadfel, S., Batouche, M.: Unsupervised image segmentation using a colony of cooperating ants. In: International Workshop on Biologically Motivated Computer Vision (2002)

    Chapter  Google Scholar 

  17. Yapa, R.D., Harada, K.: Connected component labeling algorithms for gray-scale images and evaluation of performance using digital mammograms. IJCSNS Int. J. Comput. Sci. Netw. Secur. 8(6), 33–41 (2008)

    Google Scholar 

  18. He, L., Chao, Y., Suzuki, K.: A run-based two-scan labeling algorithm. IEEE Trans. Image Process. 17(5), 749–756 (2008)

    Article  MathSciNet  Google Scholar 

  19. Constantinides, C., Chenoune, Y., Kachenoura, N., Roullot, E., Mousseaux, E., Herment, A., et al.: Semiautomated cardiac segmentation on cine magnetic resonance images using GVF–Snake deformable models. MIDAS J.—Card. MR Left Ventricle Segmentation Challenge (2009)

    Google Scholar 

  20. Schaerer, J., Casta, C., Pousin, J., Clarysse, P.: A dynamic elastic model for segmentation and tracking of the heart in MR image sequences. Med. Image Anal. 14, 738–749 (2010)

    Article  Google Scholar 

  21. Cousty, J., Najman, L., Couprie, M., Clement-Guinaudeau, S., Goissen, T., Garot, J.: Segmentation of 4D cardiac MRI: automated method based on spatio-temporal watershed cuts. Image Vis. Comput. 28, 1229–1243 (2010)

    Article  Google Scholar 

  22. Lalande, A., Salvé, N., Comte, A., Jaulent, M.C., Legrand, L., Walker, P.M., et al.: Left ventricular ejection fraction calculation from automatically selected and processed diastolic and systolic frames in short axis cine-MRI. J. Cardiovasc. Magn. Reson. 6, 817–827 (2004)

    Article  Google Scholar 

  23. Constantinides, C., Roullot, E., Lefort, M., Frouin, F.: Fully automated segmentation of the left ventricle applied to cine mr images: description and results on a database of 45 subjects. In: IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2012, pp. 3207–3210 (2012)

    Google Scholar 

  24. Lebenberg, J., Lalande, A., Clarysse, P., Buvat, I., Casta, C., Cochet, A., et al.: Improved estimation of cardiac function parameters using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging. PLoS One 10(8), e0135715 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Open University of Israel, Ranana, Israel

    Chen Avni & Maya Herman

Authors
  1. Chen Avni
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  2. Maya Herman
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Correspondence to Chen Avni .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Avni, C., Herman, M. (2020). Ensemble Approach for Left Ventricle Segmentation. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_61

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