Advertisement

Random Forest Active Learning for Retinal Image Segmentation

  • Borja AyerdiEmail author
  • Manuel Graña
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

Computer-assisted detection and segmentation of blood vessels in retinal images of pathological subjects is difficult problem due to the great variability of the images. In this paper we propose an interactive image segmentation system using active learning which will allow quick volume segmentation requiring minimal intervention of the human operator. The advantage of this approach is that it can cope with large variability in images with minimal effort. The collection of image features used for this approach is simple statistics and undirected morphological operators computed on the green component of the image. Image segmentation is produced by classification by a random forest (RF) classifier. An initial RF classifier is built from seed set of labeled points. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. We apply this approach to a well-known benchmarking dataset achieving results comparable to the state of the art in the literature.

Keywords

Image Segmentation Random Forest Matched Filter Random Forest Classifier Active Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research has been partially funded by Basque Government grant IT874-13 for the research group. Manuel Graña was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement.

References

  1. 1.
    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). http://dx.doi.org/10.1016/j.compbiomed.2006.03.003
  2. 2.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural comput. 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  3. 3.
    Anzalone, A., Bizzarri, F., Parodi, M., Storace, M.: A modular supervised algorithm for vessel segmentation in red-free retinal images. Comput. Bio. Med. 38(8), 913–922 (2008). http://dblp.uni-trier.de/db/journals/cbm/cbm38.html#AnzaloneBPS08
  4. 4.
    Barandiaran, I., Paloc, C., Graña, M.: Real-time optical markerless tracking for augmented reality applications. J. R.-Time Image Process. 5, 129–138 (2010)Google Scholar
  5. 5.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989). http://dx.doi.org/10.1109/42.34715
  8. 8.
    Cinsdikici, M.G., Aydin, D.: Detection of blood vessels in ophthalmoscope images using mf/ant (matched filter/ant colony) algorithm. Comput. Methods Programs Biomed. 96(2), 85–95 (2009). http://dblp.uni-trier.de/db/journals/cmpb/cmpb96.html#CinsdikiciA09
  9. 9.
    Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15, 201–221 (1994). doi: 10.1007/BF00993277 Google Scholar
  10. 10.
    Fraz, M.M., Barman, S., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Programs Biomed. 108(2), 600–616 (2012). http://dblp.uni-trier.de/db/journals/cmpb/cmpb108.html#FrazBRHBURO12
  11. 11.
    Geremia, E., Menze, B., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. MedicalImage Computing and Computer-Assisted Interventio—MICCAI 2010. Lecture Notes in Computer Science, vol. 6361, pp. 111–118. Springer, Heidelberg (2010)Google Scholar
  12. 12.
    Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Patt. Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  13. 13.
    Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19, 203–210 (2000)CrossRefGoogle Scholar
  14. 14.
    Jiang, X., Mojon, D.: Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 131–137 (2003). http://dx.doi.org/10.1109/TPAMI.2003.1159954
  15. 15.
    Kande, G.B., Subbaiah, P.V., Tirumala, S.S.: Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. J. Med. Syst. 34(5), 849–858 (2010). http://dblp.uni-trier.de/db/journals/jms/jms34.html#KandeST10
  16. 16.
    Lupascu, C.A., Tegolo, D., Trucco, E.: Fabc: retinal vessel segmentation using adaboost. IEEE Trans. Inf. Tech. Biomed. 14(5), 1267–1274 (2010). http://dblp.uni-trier.de/db/journals/titb/titb14.html#LupascuTT10
  17. 17.
    Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011). http://dblp.uni-trier.de/db/journals/tmi/tmi30.html#MarinAGB11
  18. 18.
    Martínez-Pérez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A., Parker, K.H.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: Taylor, C., Colchester, A (eds.) Medical Image Computing and Computer-Assisted Intervention–MICCAI’99. Lecture Notes in Computer Science, vol. 1679, pp. 90–97. Springer, Heidelberg (1999). http://dblp.uni-trier.de/db/conf/miccai/miccai1999.html#Martinez-PerezHSTBP99
  19. 19.
    Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Med. Image Anal. 11(1), 47–61 (2007). http://dblp.uni-trier.de/db/journals/mia/mia11.html#Martinez-PerezHTBP07
  20. 20.
    Martinez-Perez, M., Hughes, A., Thom, S., Parker, K.: Improvement of a retinal blood vessel segmentation method using the insight segmentation and registration toolkit (itk). IEEE Comput. Soc. 892–895 (2007). http://dx.doi.org/10.1109/IEMBS.2007.4352434
  21. 21.
    Mendonca, A.M., Campilho, A.C.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006). http://dblp.uni-trier.de/db/journals/tmi/tmi25.html#MendoncaC06
  22. 22.
    Miri, M.S., Far, A.M.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011). http://dblp.uni-trier.de/db/journals/tbe/tbe58.html#MiriM11
  23. 23.
    Ng, J., Clay, S.T., Barman, S.A., Fielder, A.R., Moseley, M.J., Parker, K.H., Paterson, C.: Maximum likelihood estimation of vessel parameters from scale space analysis. Image Vision Comput. 28(1), 55–63 (2010). http://dx.doi.org/10.1016/j.imavis.2009.04.019
  24. 24.
    Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick, J.M., Sonka, M. (eds.) SPIE Med. Imaging, vol. 5370, pp. 648–656. SPIE, USA (2004)Google Scholar
  25. 25.
    Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)CrossRefGoogle Scholar
  26. 26.
    Salem, S.A., Salem, N.M., Nandi, A.K.: Segmentation of retinal blood vessels using a novel clustering algorithm (racal) with a partial supervision strategy. Med. Biol. Eng. Comput. 45(3), 261–273 (2007). http://dblp.uni-trier.de/db/journals/mbec/mbec45.html#SalemSN07
  27. 27.
    Soares, J.V.B., Le, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J., Member, S.: Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25, 1214–1222 (2006)CrossRefGoogle Scholar
  28. 28.
    Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  29. 29.
    Tuia, D., Pasolli, E., Emery, W.: Using active learning to adapt remote sensing image classifiers. Remote Sensing of Environment (2011). http://linkinghub.elsevier.com/retrieve/pii/S0034425711001507
  30. 30.
    Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imaging Graph. 34(3), 213–227 (2010). http://dblp.uni-trier.de/db/journals/cmig/cmig34.html#VlachosD10
  31. 31.
    Xu, L., Luo, S.: A novel method for blood vessel detection from retinal images. BioMed. Eng. OnLine 9(1), 14 (2010). http://www.biomedical-engineering-online.com/content/9/1/14
  32. 32.
    Yaqub, M., Javaid, M., Cooper, C., Noble, J.: Improving the Classification Accuracy of the Classic RF Method by Intelligent Feature Selection and Weighted Voting of Trees with Application to Medical Image Segmentation. In: Suzuki, Kenji, Wang, Fei, Shen, Dinggang, Yan, Pingkun (eds.) Machine Learning in Medical Imaging. Lecture Notes in Computer Science, vol. 7009, pp. 184–192. Springer, Heidelberg (2011)Google Scholar
  33. 33.
    You, X., Peng, Q., Yuan, Y., Cheung, Y.m., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Patt. Recognit. 44(10–11), 2314–2324 (2011). http://dx.doi.org/10.1016/j.patcog.2011.01.007
  34. 34.
    Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001). http://dblp.uni-trier.de/db/journals/tip/tip10.html#ZanaK01
  35. 35.
    Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of gaussian. Comput. Biol. Med. 40(4), 438–445 (2010). http://dx.doi.org/10.1016/j.compbiomed.2010.02.008

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of CCIAComputer Intelligence Group, UPV/EHUSan SebastianSpain
  2. 2.ENGINE Centre, Wrocław University of TechnologyWrocławPoland

Personalised recommendations