Abstract
Capillary Non-Perfusion (CNP) is a condition in diabetic retinopathy where blood ceases to flow to certain parts of the retina, potentially leading to blindness. This paper presents a solution for automatically detecting and segmenting CNP regions from fundus fluorescein angiograms (FFAs). CNPs are modelled as valleys, and a novel technique based on extrema pyramid is presented for trough-based valley detection. The obtained valley points are used to segment the desired CNP regions by employing a variance-based region growing scheme. The proposed algorithm has been tested on 40 images and validated against expert-marked ground truth. In this paper, we present results of testing and validation of our algorithm against ground truth and compare the segmentation performance against two others methods.The performance of the proposed algorithm is presented as a receiver operating characteristic (ROC) curve. The area under this curve is 0.842 and the distance of ROC from the ideal point (0,1) is 0.31. The proposed method for CNP segmentation was found to outperform the watershed [1] and heat-flow [2] based methods.
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References
Gao, L., Yang, S., Xia, J., Liang, J., Qin, Y.: A new marker-based watershed algorithm. In: Proc. ISCAS, vol. 2, pp. 11–84–4 (2004)
Direkoäÿlu, C., Nixon, M.S.: Shape extraction via heat flow analogy. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 553–564. Springer, Heidelberg (2007)
Kohner, E.M.: Diabetic retinopathy. BMJ 307, 1195–1199 (1993)
Cree, M.J., Olson, J.A., McHardy, C.K., Sharp, P.F., Forresters, J.V.: The preprocessing of retinal images for the detection of fluorescein leakage. Phys. Med. Biol. 44, 293–308 (1999)
Guo, X.X., Lu, Y.N., Xu, Z.W., Pang, Y.J.: An eawa filter for denoising of filtering of fluorescein angiogram sequences. In: Proc. International Conf. on Computer and Information Technology, pp. 614–618 (2005)
Simó, A., de Ves, E.: Segmentation of macular fluorescein angiographies. a statistical approach. Pattern Recognition 34, 795–809 (2001)
Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans. on Medical Imaging 25, 1223–1232 (2006)
Hafez, M.: Using adaptive edge technique for detecting microaneurysms in fluorescein angiograms of the ocular fundus. In: Proc. Mediterranean Electrotechnical Conf., pp. 479–483 (2002)
Mendonça, A.M., Campilho, A.J.: Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients. In: Proc. International Conf. on Image Analysis and Processing, pp. 728–733 (1999)
Jagoe, R., Arnold, J., Blauth, C., Smith, P., Taylor, K.M., Wootton, R.: Measurement of capillary dropout in retinal angiograms by computerised image analysis. Pattern Recognition Letters 13, 143–151 (1992)
Gauch, J.M.: Image segmentation and analysis via multiscale gradient watershed hierarchies. IEEE Trans. on Image Processing 8, 69–79 (1999)
Karvelis, P.S., Fotiadis, D.I., Georgiou, I., Syrrou, M.: A watershed based segmentation method for multispectral chromosome images classification. In: Proc. International Conf. on EMBS, pp. 3009–3012 (2006)
Sonka, M., Fitzpatrick, J.: Hand Book of Medical Imaging. Medical Image Processing and Analysis (2004)
Beuchen, S.: The watershed transformation applied to image segmentation. Scanning microscopy international 34, 795–809 (2001)
Wang, H., Li, S.Z., Wang, Y.: Generalized quotient image. In: Proc. Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 498–505 (2004)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. International Conf. on Computer Vision, pp. 839–846 (1998)
Guo, X.X., Lu, Y.N., Xu, Z.W., Liu, Z.H., Wang, Y.X., Pang, Y.J.: Noise suppression of fluorescein angiogram sequences using bilateral filter. In: Proc. International Conf. on Machine Learning and Cybernetics, vol. 9, pp. 5366–5371 (2005)
Malladi, R., Sethian, J.A.: Fast methods for shape extraction in medical and biomedical imaging. In: Malladi, R. (ed.) Geometric Methods in Bio-medical Image Processing, pp. 49–61. Springer, Berlin (2006)
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Sivaswamy, J., Agarwal, A., Chawla, M., Rani, A., Das, T. (2008). Extraction of Capillary Non-perfusion from Fundus Fluorescein Angiogram. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2008. Communications in Computer and Information Science, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92219-3_13
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DOI: https://doi.org/10.1007/978-3-540-92219-3_13
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