DOI QR코드

DOI QR Code

Coronary Vessel Segmentation by Coarse-to-Fine Strategy using Otsu Algorithm and Decimation-Free Directional Filter Bank

  • 투고 : 2019.04.16
  • 심사 : 2019.06.18
  • 발행 : 2019.06.30

초록

In this study, a novel hierarchical approach is investigated to extract coronary vessel from X-ray angiogram. First, we propose to combine Decimation-free Directional Filter Bank (DDFB) and Homographic Filtering (HF) in order to enhance X-ray coronary angiographic image for segmentation purposes. Because the blood vessel ensures that blood flows in only one direction on vessel branch, the DDFB filter is suitable to be used to enhance the vessels at different orientations and radius. In the combination with HF filter, our method can simultaneously normalize the brightness across the image and increases contrast. Next, a coarse-to-fine strategy for iterative segmentation based on Otsu algorithm is applied to extract the main coronary vessels in different sizes. Furthermore, we also propose a new approach to segment very small vessels. Specifically, based on information of the main extracted vessels, we introduce a new method to extract junctions on the vascular tree and level of nodes on the tree. Then, the window based segmentation is applied to locate and extract the small vessels. Experimental results on our coronary X-ray angiography dataset demonstrate that the proposed approach can outperform standard method and attain the accuracy of 71.34%.

키워드

JGGJB@_2019_v23n2_557_f0001.png 이미지

Fig. 1. An illustration of the proposed method for the coronary vessel segmentation.

JGGJB@_2019_v23n2_557_f0002.png 이미지

Fig. 2. Our pre-processing process.

JGGJB@_2019_v23n2_557_f0003.png 이미지

Fig. 3. An example of diffusion on our atabase. a) Input image. b) Boundary between background and foreground based edge detector. c) Circle mask based on Hough Transform d) Diffused result from input.

JGGJB@_2019_v23n2_557_f0004.png 이미지

Fig. 4. Large vessel extraction based coarse-to-fine segmentation strategy.

JGGJB@_2019_v23n2_557_f0005.png 이미지

Fig. 5. An block diagram of the vessel enhancement based on the DDGFB and HF filter.

JGGJB@_2019_v23n2_557_f0006.png 이미지

Fig. 6. An example of coarse-to-fine segmentation strategy based on Otsu algorithm to extract the large vessels. a) Input image, b) Segmentation result.

JGGJB@_2019_v23n2_557_f0007.png 이미지

Fig. 7. A block diagram of our method for small vessel extraction.

JGGJB@_2019_v23n2_557_f0008.png 이미지

Fig. 8. An example of connecting the nearest the center-lines. a) Input image, b) Image after connecting the nearest the center-lines

JGGJB@_2019_v23n2_557_f0009.png 이미지

Fig. 9. An example of tracing the vessel branch and junction. a) The seed point, b) Tracking candidate points and the junction

JGGJB@_2019_v23n2_557_f0010.png 이미지

Fig. 10. An example of eliminating the invalid branches. In this case, the branches corresponding to directions $\overrightarrow{v_1}$ and $\overrightarrow{v_3}$ are eliminated.

JGGJB@_2019_v23n2_557_f0011.png 이미지

Fig. 11. An example of the level of the vessel branch and degree of nodes.

JGGJB@_2019_v23n2_557_f0012.png 이미지

Fig. 12. An example of the level of the vessel tree and degree of nodes on our dataset

Algorithm 2. Small vessel detection and segmentation based on window analysis.

JGGJB@_2019_v23n2_557_f0013.png 이미지

JGGJB@_2019_v23n2_557_f0014.png 이미지

Fig. 15. Small vessel extraction. a) Part of input image, b) Result from large vessel extraction approach, c) Result from small vessel extraction approach

JGGJB@_2019_v23n2_557_f0015.png 이미지

Fig. 16. An image IM-0001-000130 in our dataset.

JGGJB@_2019_v23n2_557_f0016.png 이미지

Fig. 17. Ground truth of the IM-0001-000130 image.

JGGJB@_2019_v23n2_557_f0017.png 이미지

Fig. 18. Segmentation result of the proposed approach on our dataset. a) Input image, b) Segmented vessels, c) Ground truth

JGGJB@_2019_v23n2_557_f0018.png 이미지

Fig. 19. Some false alarms of the proposed approach on our dataset. a) Input image b) False alarms from segmented result, c) Ground truth

JGGJB@_2019_v23n2_557_f0019.png 이미지

Fig. 20. Comparison of the segmentation results on our dataset a) Input image, b) Our result, c) Result in [18]

JGGJB@_2019_v23n2_557_f0020.png 이미지

Fig. 13. An example of window constructed between two nodes.

JGGJB@_2019_v23n2_557_f0021.png 이미지

Fig. 14. An example of contrast enhancement in the window analysis. a) Input image, b) Result from contrast enhancement

Table 1. The optimal parameters for the DDFB in our experiments.

JGGJB@_2019_v23n2_557_t0001.png 이미지

Table 2. Comparison performance of the coronary vessel segmentation on our dataset.

JGGJB@_2019_v23n2_557_t0002.png 이미지

Algorithm 1. Branch and junction detection

JGGJB@_2019_v23n2_557_t0003.png 이미지

참고문헌

  1. M. T. Dehkordi, S. Sadri, and A. Doosthoseini, "A review of coronary vessel segmentation algorithms," Journal of Medical Signals and Sensors, vol.1, no.1, pp.49-54, 2011. https://doi.org/10.4103/2228-7477.83519
  2. C. Kirbas and F. Quek, "A review of vessel extraction techniques and algorithms," ACM Computing Surveys, vol.36, no.2, pp.81-121, 2004. DOI: 10.1145/1031120.1031121
  3. A. Sarwal and A. Dhawan, "3-D reconstruction of coronary arteries," Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol.1, pp.504-505, 1994. DOI: 10.1109/IEMBS.1994.411932
  4. R. Liao, D. Luc, Y. Sun, and K. Kirchberg, "3-D reconstruction of the coronary artery tree from multiple views of a rotational X-ray angiography," The International Journal of Cardiovascular Imaging, vol.26, no.7, pp.733-749, 2010. DOI: 10.1007/s10554-009-9528-0
  5. J. O'Brien and N. Ezquerra, "Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial and structural constraints," Visualization in Biomedical Computing, Rochester, 1994. DOI: 10.1117/12.185183
  6. C. Molina, G. Prause, P. Radeva, and M. Sonka, "3-D catheter path reconstruction from biplane angiograms," in SPIE, vol.3338, pp.504-512, 1998. DOI: 10.1117/12.310929
  7. R. R. Petrocelli, J. Elion, and K. M. Manbeck, "A new method for structure recognition in unsubtracted digital angiograms," in IEEE Computers in Cardiology, pp.207-210, 1992. DOI: 10.1109/CIC.1992.269410
  8. S. Lu and S. Eiho, "Automatic detection of the coronary arterial contours with sub-branches from an x-ray angiogram," IEEE Computers in Cardiology, pp. 575-578, 1993. DOI: 10.1109/CIC.1993.378337
  9. V. Bombardier, M. C. Jaluent, A. Bubel, and J. Bremont, "Cooperation of two fuzzy segmentation operators for digital subtracted angiograms analysis," IEEE Conference on Fuzzy Systems, vol.2, pp. 1057-1062, 1997. DOI: 10.1109/FUZZY.1997.622856
  10. R. Poli and G. Vall, "An algorithm for real-time vessel enhancement and detection," Comput Meth Prog Biomed, vol.52, no.1, pp.1-22, 1997. DOI: 10.1016/S0169-2607(96)01773-7
  11. S. Eiho and Y. Qian, "Detection of coronary artery tree using morphological operator," Proc. IEEE Comput. Cardiol, pp.525-528, 1997. DOI: 10.1109/CIC.1997.647950
  12. F. Zana and J. C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," IEEE Trans Image Process, vol.10, no.7, pp.1010-1019, 2001. DOI: 10.1109/83.931095
  13. C. Yan, S. Hirano, and Y. Hata, "Extraction of blood vessel in CT angiography image aided by fuzzy logic," Proc. IEEE Int. Conf. Signal Processing, pp.926-929, 2000. DOI: 10.1109/ICOSP.2000.891673
  14. M. Orkisz, C. Bresson, I. Magnin, O. Champin, and P. Douek, "Improved vessel visualization in MR angiography by nonlinear anisotropic filtering," MagnReson Med, vol.37, no.6, pp.914-919, 1997. DOI: 10.1002/mrm.1910370617
  15. A. Frangi, W. Niessen, K. Vincken, and M. Viergever, "Multiscale vessel enhancement filtering," Medical Image Computing Computer-Assisted Intervention. Lect Notes ComputSci, vol.1496, pp.130-137, 1998. DOI: 10.1007/BFb0056195
  16. C. Lorenz, I. C. Carlsen, T. Buzug, C. Fassnacht, and J. Weese, "A multiscale line filter with automatic scale selection based on the Hessian matrix for medical image segmentation," Proc. Scale-Space Theories in Computer Vision, LNCS, vol.1252, pp.152-163, 1997. DOI: 10.1007/3-540-63167-4_47
  17. G. Agam, S. G. Armato, C. Wu, "Vessel tree reconstruction in thoracic CT scans with application to nodule detection," IEEE Trans Med Imaging, vol.24, no.4, pp.486-499, 2005. DOI: 10.1109/TMI.2005.844167
  18. P. T. H. Truc, M. A. Khan, Y. K. Lee, S. Lee and T. S. Kim, "Vessel enhancement filter using directional filter bank," Computer Vision and Image Understanding, vol.113, no.1, pp.101-112, 2009. DOI: 10.1016/j.cviu.2008.07.009
  19. R. Gonzalez, R. Woods, Digital Image Processing, Prentice Hall, New Jersey, USA, 2002.
  20. H. K. Yuen, J. Princen, J. Illingworth, and J. Kittler, "Comparative study of Hough Transform methods for circle finding," Image and Vision Computing, vol.8, no.1, pp.71-77, 1990. DOI: 10.1016/0262-8856(90)90059-E
  21. N. Huynh, "A filter bank approach to automate vessel extraction with applications," Master Thesis, University of California, 2013.
  22. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol.9, no.1, pp. 62-66, 1979. DOI: 10.1109/TSMC.1979.4310076
  23. V. Mohan, el. al., "Vessel segmentation with automatic centerline extraction using tubular tree segmentation," Workshop on Cardiovascular Interventional Imaging and Biophysical Modeling, pp.1-8, 2009.
  24. J. Sosa, "MIPAR - Premier Image Analysis & Image Segmentation Software," [Online]. Available: https://www.mipar.us/.
  25. K. H. Zou, el. al., "Statistical validation of image segmentation quality based on a spatial overlap index," Academic Radiology, vol.11, no.2, pp.178-189, 2004. DOI: 10.1016/S1076-6332(03)00671-8