An ensemble-based microaneurysm detector for retinal images

The key to the early detection of diabetic retinopathy is to recognize microaneurysms in the fundus of the eye in time. Reliable detection of such lesions is still an open issue in medical image processing. We propose a framework which assembles several candidate extractors and preprocessing methods to strengthen the detection accuracy of the individual approaches. We use a simulated annealing based search approach to select an optimal combination from the available methods. The proposed method has proved its superiority over the individual algorithms in an online competition dedicated to the objective comparison of microaneurysm detectors.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  Alin Achim,et al.  18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011 , 2011, ICIP.

[3]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[4]  Thomas Walter,et al.  Automatic Detection of Microaneurysms in Color Fundus Images of the Human Retina by Means of the Bounding Box Closing , 2002, ISMDA.

[5]  P F Sharp,et al.  An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. , 1996, Computers and biomedical research, an international journal.

[6]  S. Abdelazeem,et al.  Micro-aneurysm detection using vessels removal and circular Hough transform , 2002, Proceedings of the Nineteenth National Radio Science Conference.

[7]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[8]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[9]  Qin Li,et al.  Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..

[10]  Kuo-Liang Chung,et al.  An Efficient Randomized Algorithm for Detecting Circles , 2001, Comput. Vis. Image Underst..

[11]  Michael H. Goldbaum,et al.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels , 2003, IEEE Transactions on Medical Imaging.

[12]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[13]  Anurag Mittal,et al.  Automated feature extraction for early detection of diabetic retinopathy in fundus images , 2009, CVPR.

[14]  Patrick Pérez,et al.  Object removal by exemplar-based inpainting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..