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Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern

  • Arun BalodiEmail author
  • R. S. Anand
  • M. L. Dewal
  • Anurag Rawat
Original Article
  • 33 Downloads

Abstract

This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.

Keywords

Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system 

Notes

Acknowledgements

The author would like to thank the Ministry of Human Resource Development, Government of India, for providing financial assistance. Authors also thank the Indian Institute of Technology, Roorkee, India, for providing research facilities. The authors would also like to extend the deepest and sincere appreciations to the Department of Cardiology, Swami Rama Himalayan University, Dehradun, India, for providing the dataset of ultrasound images and their constant support for carrying out this research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study has been approved by the ethics committee of Swami Rama Himalayan University, Dehradun, India, and has been performed in accordance with the ethical standards.

References

  1. 1.
    Kaddoura S (2012) Echo made easy. Elsevier Health Sciences, AmsterdamGoogle Scholar
  2. 2.
    Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M (2006) Burden of valvular heart diseases: a population-based study. Lancet 368(9540):1005–1011CrossRefGoogle Scholar
  3. 3.
    Utsunomiya T, Ogawa T, Doshi R, Patel D, Quan M, Henry WL, Gardin JM (1991) Doppler color flow “proximal isovelocity surface area” method for estimating volume flow rate: effects of orifice shape and machine factors. J Am Coll Cardiol 17(5):1103–1111CrossRefGoogle Scholar
  4. 4.
    Hall SA, Brickner ME, Willett DL, Irani WN, Afridi I, Grayburn PA (1997) Assessment of mitral regurgitation severity by Doppler color flow mapping of the vena contracta. Circulation 95(3):636–642CrossRefGoogle Scholar
  5. 5.
    Nishimura RA, Otto CM, Bonow RO, Carabello BA, Erwin JP, Guyton RA, O’Gara PT, Ruiz CE, Skubas NJ, Sorajja P et al (2014) AHA/ACC guideline for the management of patients with valvular heart disease: executive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. J Am Coll Cardiol 63(22):2438–2488CrossRefGoogle Scholar
  6. 6.
    Tribouilloy C, Shen WF, Quéré J-P, Rey J-L, Choquet D, Dufosse H, Lesbre J-P (1992) Assessment of severity of mitral regurgitation by measuring regurgitant jet width at its origin with transesophageal Doppler color flow imaging. Circulation 85(4):1248–1253CrossRefGoogle Scholar
  7. 7.
    Schwammenthal E, Chen C, Giesler M, Sagie A, Guerrero JL, Vazquez de Prada JA, Hombach V, Weyman AE, Levine RA (1996) New method for accurate calculation of regurgitant flow rate based on analysis of Doppler color flow maps of the proximal flow field validation in a canine model of mitral regurgitation with initial application in patients. J Am Coll Cardiol 27(1):161–172CrossRefGoogle Scholar
  8. 8.
    Biner S, Rafique A, Rafii F, Tolstrup K, Noorani O, Shiota T, Gurudevan S, Siegel RJ (2010) Reproducibility of proximal isovelocity surface area, vena contracta, and regurgitant jet area for assessment of mitral regurgitation severity. JACC Cardiovasc Imaging 3(3):235–243CrossRefGoogle Scholar
  9. 9.
    Grayburn PA, Fehske W, Omran H, Brickner ME, Lüderitz B (1994) Multiplane transesophageal echocardiographic assessment of mitral regurgitation by Doppler color flow mapping of the vena contracta. Am J Cardiol 74(9):912–917CrossRefGoogle Scholar
  10. 10.
    Obayya M, Abou-Chadi F (2008) Data fusion for heart diseases classification using multi-layer feed forward neural network. IEEE Int Conf Comput Eng Syst ICCES 2008:67–70Google Scholar
  11. 11.
    Maglogiannis I, Loukis E, Zafiropoulos E, Stasis A (2009) Support vectors machine-based identification of heart valve diseases using heart sounds. Comput Methods Progr Biomed 95(1):47–61CrossRefGoogle Scholar
  12. 12.
    Shuping S, Haibin W, Jiang Z, Fang Y, Tao T (2014) Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system. Expert Syst Appl 41(4):1769–1780CrossRefGoogle Scholar
  13. 13.
    Ghadiri Hedeshi N, Saniee Abadeh M (2014) Coronary artery disease detection using a fuzzy-boosting PSO approach. Comput Intell Neurosci.  https://doi.org/10.1155/2014/783734
  14. 14.
    Gharehbaghi A, Ask P, Babic A (2015) A pattern recognition framework for detecting dynamic changes on cyclic time series. Pattern Recognit 48(3):696–708CrossRefGoogle Scholar
  15. 15.
    Moghaddasi H, Nourian S (2016) Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med 73:47–55CrossRefGoogle Scholar
  16. 16.
    Balodi A, Dewal ML, Anand RS, Rawat A (2016) Texture based classification of the severity of mitral regurgitation. Comput Biol Med 73:157–164CrossRefGoogle Scholar
  17. 17.
    Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A (2003) Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging 22(7):902–912CrossRefGoogle Scholar
  18. 18.
    Huang Y-L, Wang K-L, Chen Dar-Ren (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2):164–169CrossRefGoogle Scholar
  19. 19.
    Mohanty AK, Senapati MR, Lenka SK (2013) A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput Appl 22(6):1151–1161CrossRefGoogle Scholar
  20. 20.
    Wang Z, Qixun Q, Ge Y, Kang Y (2016) Breast tumor detection in double views mammography based on extreme learning machine. Neural Comput Appl 27(1):227–240CrossRefGoogle Scholar
  21. 21.
    Ojala T, Pietikäinen M, Harwood David (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59CrossRefGoogle Scholar
  22. 22.
    Ojala T, Pietikäinen M, Mäenpää Topi (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  23. 23.
    Mäenpää T (2003) The local binary pattern approach to texture analysis: extensions and applications. Oulun yliopisto, OuluGoogle Scholar
  24. 24.
    Oliver A, Lladó X, Freixenet J, Martí J (2007) False positive reduction in mammographic mass detection using local binary patterns. Med Image Comput Comput Assist Interv MICCAI 2007:286–293Google Scholar
  25. 25.
    Devrim U, Ahmet E, Jasinschi Radu S (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans Inf Technol Biomed 14(4):897–903CrossRefGoogle Scholar
  26. 26.
    Anderson M, Motta R, Chandrasekar S, Stokes M (1996) Proposal for a standard default color space for the internet. In: SRGB, color and imaging conference, pp 238–245Google Scholar
  27. 27.
    Burt Peter J, Adelson Edward H (1983) A multiresolution spline with application to image mosaics. ACM Trans Graph (TOG) 2(4):217–236CrossRefGoogle Scholar
  28. 28.
    Qian X, Hua X-S, Ping C, Liangjun K (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44(10–11):2502–2515CrossRefGoogle Scholar
  29. 29.
    Xiaoqiang L, Li X (2014) Multiresolution imaging. IEEE Trans Cybern 44(1):149–160CrossRefGoogle Scholar
  30. 30.
    Adelson EH, Anderson CH, Bergen JR, Burt PJ, Ogden JM (1984) Pyramid methods in image processing. RCA Eng 29(6):33–41Google Scholar
  31. 31.
    Burt PJ (1981) Fast filter transform for image processing. Comput Graph Image Process 16(1):20–51CrossRefGoogle Scholar
  32. 32.
    Pietikäinen M, Ojala T, Zelin Xu (2000) Rotation-invariant texture classification using feature distributions. Pattern Recognit 33(1):43–52CrossRefGoogle Scholar
  33. 33.
    Heikkilä M, Pietikäinen M, Schmid Cordelia (2009) Description of interest regions with local binary patterns. Pattern Recognit 42(3):425–436CrossRefGoogle Scholar
  34. 34.
    Ahonen T, Matas J, Chu H, Matti P (2009) Rotation invariant image description with local binary pattern histogram Fourier features. In: Scandinavian conference on image analysis, pp 61–70Google Scholar
  35. 35.
    Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetCrossRefGoogle Scholar
  36. 36.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  37. 37.
    Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  38. 38.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  39. 39.
    Azar AT, El-Metwally SM (2013) Decision tree classifiers for automated medical diagnosis. Neural Comput Appl 23(7–8):2387–2403CrossRefGoogle Scholar
  40. 40.
    Geisser S (1993) Predictive inference, vol 55. CRC press, Boca RatonCrossRefGoogle Scholar
  41. 41.
    Prasad AM, Iverson LR, Liaw Andy (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181–199CrossRefGoogle Scholar
  42. 42.
    Ai F, Bin J, Zhang Z, Huang J, Wang J, Liang Y, Yu L, Zhen Y (2014) Application of random forests to select premium quality vegetable oils by their fatty acid composition. Food Chem 143:472–478CrossRefGoogle Scholar
  43. 43.
    Yadav AR, Anand RS, Dewal ML, Gupta S (2015) Hardwood species classification with DWT based hybrid texture feature extraction techniques. Sadhana 40(8):2287–2312MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIndian Institute of TechnologyRoorkeeIndia
  2. 2.Department of Electrical EngineeringGraphic Era UniversityDehradunIndia
  3. 3.Department of CardiologySwami Rama Himalayan UniversityDehradunIndia

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