Skip to main content

Advertisement

Log in

Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Epicardial adipose tissue is a visceral fat that has remained an entity of concern for decades owing to its high correlation with coronary heart disease. It continues to stump medical practitioners on the pretext of its relevance with pericardial fat and its dependence on a numerous other parameters including ethnicity and physique of an individual. This calls for a fool-proof algorithm that promises accurate classification and segmentation, hence an immaculate prediction. CT is immensely popular and widely preferred for diagnosis. Implementation of an improvised algorithm in CT would be a natural necessity. This research work proposes a Fruitfly Algorithm based Modified region growing algorithm is applied to the acquired CT images to segment fat accurately. The proposed methodology promises image registration and classification in order to segment two cardiac fats namely epicardial, pericardial and mediastinal. The main contributions are (1) Fat feature extraction: Construction of GLCM features CT image (2) Development of GWO based optimal neural network for classification; (3) Modeling the fat segmentation using modified region growing algorithm with Fruitfly optimization. The entire experimentation has been implemented in MATLAB simulation environment and final result is expected to flaunt a definite distinction between cardiac mediastinal and epicardial fats. Parallely, the accuracy, sensitivity, specificity, FPR and FNR have been stated and contrasted methodically with the existing methodology. This venture aims at spurring the healthcare industry towards smarter computational techniques that multiplies efficiency manifold.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Sicari, R., Sironi, A. M., Petz, R., Frassi, F., Chubuchny, V., De Marchi, D., Positano, V., Lombardi, M., Picano, E., and Gastaldelli, A., Pericardial rather than epicardial fat is a cardiometabolic risk marker: An MRI vs echo study. J. Am. Soc. Echocardiogr. 24(10):1156–1162, 2011.

    Article  Google Scholar 

  2. Sacks, H. S., and Fain, J. N., Human epicardial adipose tissue: A review. Am. Heart J. 153(6):907–917, 2007.

    Article  CAS  Google Scholar 

  3. Ding, X., Terzopoulos, D., Diaz-Zamudio, M., Berman, D.S., Slomka P.J., and Dey, D., Automated epicardial fat volume quantification from non-contrast CT. J. Med. Imag., 90340I-9034, 2014.

  4. Pontone, G., Andreini, D., Bertella, E., Petullà, M., Russo, E., Innocenti, E., and Mushtaq, S., Comparison of cardiac computed tomography versus cardiac magnetic resonance for characterization of left atrium anatomy before radiofrequency catheter ablation of atrial fibrillation. Int. J. Cardiol. 179:114–112, 2015.

    Article  Google Scholar 

  5. Oda, S., Utsunomiya, D., Funama, Y., Yuki, H., Kidoh, M., Nakaura, T., and Takaoka, H., Effect of iterative reconstruction on variability and reproducibility of epicardial fat volume quantification by cardiac CT. J. Cardiovasc. Comput. Tomograp. 10(2):150–155, 2016.

    Article  Google Scholar 

  6. Marwan, M., and Achenbach, S., Quantification of epicardial fat by computed tomography: Why, when and how? J. Cardiovasc. Comput. Tomograp. 7(1):3–10, 2013.

    Article  Google Scholar 

  7. Apfaltrer, P., Schindler, A., Schoepf, U. J., Nance, J. W., Tricarico, F., Ebersberger, U., and McQuiston, A. D., Comparison of epicardial fat volume by computed tomography in black versus white patients with acute chest pain. Am. J. Cardiol. 113(3):422–428, 2015.

    Article  Google Scholar 

  8. Aslanabadi, N., Salehi, R., Javadrashid, A., Tarzamni, M., Khodadad, B., Enamzadeh, E., and Montazerghaem, H., Epicardial and pericardial fat volume correlate with the severity of coronary artery stenosis. J. Cardiovasc. Thorac. Res. 6(4):235–239, 2014.

    Article  Google Scholar 

  9. Yalamanchilia, R., Dey, D., Kurkure, U., Nakazato, R., Berman, D. S., and Kakadiaris, I. A., Knowledge-based quantification of pericardial fat in non-contrast CT data. Proc. SPIE 7623:76231X–762317X.

  10. Rodrigues, É. O., Conci, A., Morais, F. F. C., and Pérez, M. G., Towards the automated segmentation of epicardial and mediastinal fats. 2015 Elsevier Ireland Ltd. All rights reserved.

  11. Lin, D.-T., Yan, C.-R., and Chen, W.-T., Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system. J. Comput. Med. Imag. Graph. 29(6):447–458, 2005.

    Article  Google Scholar 

  12. Ling, Z., McManigle, J., Zipunnikov, V., Pashakhanloo, F., Khurram, I. M., Zimmerman, S. L., and Philips, B., The association of left atrial low-voltage regions on electro anatomic mapping with low attenuation regions on cardiac computed tomography perfusion imaging in patients with atrial fibrillation. J. Heart Rhythm 12(2):857–864, 2015.

    Article  Google Scholar 

  13. Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., and Van Ginneken, B., Multi-atlas-based segmentation with local decision fusion—Application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7):1000–1010, 2009.

    Article  Google Scholar 

  14. Rodrigues, É. O., Morais, F. F. C., Morais, N. A. O. S., Conci, L. S., Neto, L. V., and Conci, A., A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography. Comput. Methods Prog. Biomed. 123:109–128, 2016.

    Article  CAS  Google Scholar 

  15. Champier, J., Cinotti, L., Bordet, J.-C., Lavenne, F., and Mallet, J.-J., Delineation and quantization of brain lesions by fuzzy clustering in positron emission tomography. J. Comput. Med. Imaging Graph. 20(1):31–41, 2006.

    Google Scholar 

  16. Bandekar, A. N., Naghavi, M. and Kakadiaris, I. A., Automated pericardial fat quantification in CT data. Proceedings of the 28th IEEE EMBS annual international conference new York City, USA, 2006.

  17. Guo, Z., Xin, Y., Liu, S., Lv, X. and Li, S., Comparisons of fat quantification methods based on MRI segmentation. IEEE, 978–1–4799-3979-4/14/$31.00, 2014.

  18. Zlokolica, V., Velicki, L., Janev, M., Mitrinovic, D., and Babin, D., Epicardial fat registration by local adaptive morphology-thresholding based 2D segmentation. IEEE, 2014.

  19. Sumitpaibul, P., Damrongphithakkul, A., and Watchareeruetai, U., Fat detection algorithm for liver biopsy images. IEEE, 978–1–4799-3174-3/14/$31.00, 2014.

  20. Antony, J., McGuinness, K., Welch, N., Coyle, J., and Franklyn-Miller, A., Fat quantification in MRI-defined lumbar muscles. IEEE, 978–1–4799-6463-5/14/$31.00, 2014.

  21. Yan, Z., Tan, C., Zhang, S., Zhou, Y., and Belaroussi, B., Automatic liver segmentation and hepatic fat fraction assessment in MRI. IEEE, 1051–4651/14 $31.00, 2014.

  22. Ahmed, S., Gilles, B., Puech, W., Hassouni, M. E and Rziza, M., Hierarchical MRI segmentation of the musculoskeletal system using texture analysis and topological constraints. 978–1–4799-4572-6/14/$31.00, 2014.

  23. Song, H., Zhang, Q., Sun, F., Wang, J., and Wang, Q., Breast tissue segmentation on MR images using KFCM with spatial constraints. IEEE, 978–1–4799-5464-3/14/$31.00, 2014.

  24. Ahmad, E., Yap, M. H., Degens, H. and McPhee, J., Enhancement of MRI human thigh muscle segmentation by template-based framework. IEEE, 978–1–4799-5686-9/14/$31.00, 2014.

  25. Spasojeviü, A., Stojanov, O., Turukalo, T. L., and Šveljo, O., Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images. IEEE, 978–1–4799-5888-7/14/$31.00, 2014.

  26. Ayerdi, B., Echaniz, O., Savio, A., and Graña, M., Automated segmentation of subcutaneous and visceral adipose tissues from MRI. Springer. 427–433, 2016.

  27. Brinkley, T. E., Tina, E., Hsu, F.-C., Jeffrey Carr, J., Gregory Hundley, W., Bluemke, D. A., Polak, J. F., and Ding, J., Pericardial fat is associated with carotid stiffness in the multi-ethnic study of atherosclerosis. J. Nutr. Metab. Cardiovasc. Dis. 21(5):332–338, 2011.

    Article  CAS  Google Scholar 

  28. Bandekar, A. N., Naghavi, M., and Kakadiaris, I. A., Automated pericardial fat quantification in CT data. Eng. Med. Biol. Soc. Int. Conf. IEEE. 932–935, IEEE, 2006.

  29. Kaus, M. R., von Berg, J., Weese, J., Niessen, W., and Pekar, V., Automated segmentation of the left ventricle in cardiac MRI. J. Med. Image Anal. 8(3):245–254, 2004.

    Article  Google Scholar 

  30. Premkumar, R., and Anand, S., Secured and compound 3-D chaos image encryption using hybrid mutation and crossover operator. Multimed. Tools Applic., 2018. https://doi.org/10.1007/s11042-018-6534-z.

  31. Mangili, L. C., Mangili, O. C., Bittencourt, M. S., Miname, M. H., Harada, P. H., Lima, L. M., Rochitte, C. E., and Santos, R. D., Epicardial fat is associated with severity of subclinical coronary atherosclerosis in familial hypercholesterolemia. J. Atherosclero. 254:73–77, 2016.

    Article  CAS  Google Scholar 

  32. Meenakshi, K., Rajendran, M., Srikumar, S., Chidambaram, S., Epicardial fat thickness: A surrogate marker of coronary artery disease – Assessment by echocardiography. 2015 cardio logical Society of India, published by Elsevier B. V, All rights reserved.

  33. Sicari, R., Sironi, A. M., Petz, R., Frassi, F., Chubuchny, V., Marchi, D.D., Positano, V., Lombardi, M., Picano, E., and Gastaldelli, A., Pericardial rather than Epicardial fat is a Cardiometabolic risk marker: An MRI vs Echo study. J. Am. Soc. Echocardiogr., 24(10).

    Article  Google Scholar 

  34. Yang, F., Ding, M., Zhang, X., Hou, W., and Zhong, C., Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization. J. Inf. Sci. 316:440–456, 2015.

    Article  Google Scholar 

  35. Guo, Z., Xin, Y., Liu, S., Lv, X. and Li, S., Comparisons of fat quantification methods based on MRI segmentation. IEEE, 978–1–4799-3979-4/14/$31.00, 2014.

  36. Bucher, A. M., Joseph Schoepf, U., Krazinski, A. W., Silverman, J., Spearman, J. V., De Cecco, C. N., Meinel, F. G., Vogl, T. J., and Geyer, L. L., Influence of technical parameters on epicardial fat volume quantification at cardiac CT. Eur. J. Radiol. 84(6):1062–1067, 2015.

    Article  Google Scholar 

  37. Premkumar, R., and Anand, S., Secured permutation and substitution based image encryption algorithm for medical security application. J. Med. Imag. Health Inform. 6(8):2012–2018, 2016.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Priya.

Ethics declarations

Conflict of Interest

The authors have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priya, C., Sudha, S. Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network. J Med Syst 43, 104 (2019). https://doi.org/10.1007/s10916-019-1227-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1227-3

Keywords

Navigation