Advertisement

A Novel Curvature Feature Embedded Level Set Method for Image Segmentation of Coronary Angiograms

  • Mehboob Khokhar
  • Shahnawaz Talpur
  • Sunder Ali Khowaja
  • Rizwan Ali Shah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Segmentation methods in medical image processing are usually distorted by low contrast and intensity inhomogeneity. There are several image segmentation methods which are based on region based segmentation. But these algorithms mostly depend on the quality of the image. This paper gives an improved level set method for image segmentation to reduce the effect of noise. In order to achieve this, curvature feature energy function in standard level set energy function has been used. The proposed method is being applied on heart angiograms provided by Cardiac Department ISRA University Hospital, Pakistan. Extensive evaluation of these images depicts the robustness and efficiency of the proposed method over the previous work. Moreover, this method gives better trade-off between accuracy and implementation time over the related work.

Keywords

Image segmentation Heart angiograms Level set Medical image processing 

References

  1. 1.
    Shen, G.J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Lu, X., Li, X.: Group sparse reconstruction for image segmentation. Neurocomputing 136, 41–48 (2014)CrossRefGoogle Scholar
  3. 3.
    Khowaja, S.A., Unar, M.A., Ismaili, I.A.: Supervised method for blood vessel segmentation from coronary angiogram images using 7-D feature vector. Imaging Sci. J. 64(04), 196–203 (2016)CrossRefGoogle Scholar
  4. 4.
    Jiang, X., Mojo, D.: Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Patt. Anal. 25(1), 131–137 (2003)CrossRefGoogle Scholar
  5. 5.
    Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Ann. Rev. Biomed. Eng. 2, 315–337 (2000)CrossRefGoogle Scholar
  6. 6.
    Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Tran. Image Process. 17(11), 2029–2039 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Sclaroff, S., Isidoro, J.: Active blobs: region-based, deformable appearance models. Comput. Vis. Image Underst. 89, 197–225 (2003)CrossRefGoogle Scholar
  8. 8.
    Taghizadeh Dehkordi, M., Hoseini, A.M.D., Sadri, S., Soltanianzadeh, H.: Local feature fitting active contour for segmenting vessels in angiograms. IET Comput. Vis. 8(3), 161–170 (2014)CrossRefGoogle Scholar
  9. 9.
    Li, C., Kao, C. Y., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentationGoogle Scholar
  10. 10.
    Zhang, K., Song, H., Zhang, L.: Active contours driven by local image fitting energy. Patt. Recogn. 43(4), 1199–1206 (2010)CrossRefGoogle Scholar
  11. 11.
    Zhang, B., Wu, X., You, J., Li, Q., Karray, F.: Detection of micro-aneurysms using multi-scale correlation coefficients. Patt. Recogn. 43(6), 2237–2248 (2010)CrossRefGoogle Scholar
  12. 12.
    Salazar-Gonzalez, A.G., Li, Y., Liu, X.: Retinal blood vessel segmentation via graph cut. In: 11th IEEE International Conference on Control Automation Robotics and Vision (ICARCV), pp. 225–230, December 2010Google Scholar
  13. 13.
    Sun, K., Chen, Z., Jiang, S.: Local morphology fitting active contour for automatic vascular segmentation. IEEE Trans. Biomed. Eng. 59(2), 464–473 (2012)CrossRefGoogle Scholar
  14. 14.
    Lugauer, F., Zhang, J., Zheng, Y.: Improving accuracy in coronary lumen segmentation via explicit calcium exclusion, learning-based ray detection and surface optimization. In: Medical Imaging 2014: Image Processing, p. 90343U, 21 March 2014Google Scholar
  15. 15.
    Huang, Q., Bai, X., Li, Y., Jin, L., Li, X.: Optimized graph-based segmentation for ultrasound images. Neurocomputing 129, 216–224 (2014)CrossRefGoogle Scholar
  16. 16.
    Shen, J., Du, Y., Li, X.: Interactive segmentation using constrained Laplacian optimization. IEEE Trans. Circ. Syst. Video Technol. 24(7), 1088–1100 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, K., Liu, Q., Song, H., Li, X.: A variational approach to simultaneous image segmentation and bias correction. IEEE Trans. Cybern. (2014).  https://doi.org/10.1109/TCYB.2014.2352343CrossRefGoogle Scholar
  18. 18.
    Zhou, H., Li, X., Schaefer, G., Celebi, E.: Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Underst. 117(9), 1004–1016 (2013)CrossRefGoogle Scholar
  19. 19.
    Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66(5), 1632–1648 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Cai, X.H., Chan, R., Zeng, T.Y.: A two-stage image segmentation method using a convex variant of the Mumford-Shah model and thresholding. SIAM J. Imaging Sci. 6(1), 368–390 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang, S., Li, B., Zhou, S.: A segmentation method of coronary angiograms based on multi-scale filtering and region-growing. In: International Conference on Biomedical Engineering and Biotechnology (iCBEB), pp. 678–681. IEEE, May 2012Google Scholar
  22. 22.
    Kang, W., Kang, W., Li, Y., Wang, Q.: The segmentation method of degree-based fusion algorithm for coronary angiograms. In: 2013 AQ5 International Conference on Measurement, Information and Control (ICMIC) (2013)Google Scholar
  23. 23.
    Qian, Y., Eiho, S., Sugimoto, N., Fujita, M.: Automatic extraction of coronary artery tree on coronary angiograms by morphological operators. In: Computers in Cardiology 1998, pp. 765–768. IEEE, September 1998Google Scholar
  24. 24.
    Chanwimaluang, T., Fan, G., Fransen, S.R.: Hybrid retinal image registration. IEEE Trans. Inf Technol. Biomed. 10(1), 129–142 (2006)CrossRefGoogle Scholar
  25. 25.
    Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 37(2), 262–267 (2007)CrossRefGoogle Scholar
  26. 26.
    Kaang, W., Wang, K., Chen, W., Kang, W.: Segmentation method based on fusion algorithm for coronary angiograms. In: 2nd International AQ4 Congress on Image and Signal Processing, 2009. CISP 2009, pp. 1–4. IEEE, October 2009Google Scholar
  27. 27.
    Yang, Y., Tannenbaum, A., Giddens, D., Stillman, A.: Automatic segmentation of coronary arteries using bayesian driven implicit surfaces. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007, pp. 189–192. IEEE, April 2007Google Scholar
  28. 28.
    Cruz-Aceves, I., Hernandez-Aguirre, A., Valdez, S.I.: On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters. Appl. Soft Comput. 46, 665–676 (2016)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Liatsis, P.: Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts. IEEE Trans. Inf Technol. Biomed. 16(4), 782–788 (2012)CrossRefGoogle Scholar
  30. 30.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: 1998 Medical Image Computing and Computer-Assisted Intervention, MICCAI 1998, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mehboob Khokhar
    • 1
  • Shahnawaz Talpur
    • 1
  • Sunder Ali Khowaja
    • 2
  • Rizwan Ali Shah
    • 1
  1. 1.Mehran University of Engineering and TechnologyJamshoroPakistan
  2. 2.Institute of Information and Communication TechnologyUniversity of SindhJamshoroPakistan

Personalised recommendations