Diagnosis of Diabetic Retinopathy

Bright lesions, in the form of exudates and cotton wool spots, and dark lesions, such as hemorrhages, are indicators of the disease. Challenges in this field include segmentation of these features, as well as blood vessels. The literature has used various techniques such as matched-filtering, morphological operations, scale-spaced region growing, edge detection, local thresholding, nearest neighbor pixel segmentation, and neural network pixel segmentation.

[1]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[2]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[3]  Roberto Marcondes Cesar Junior,et al.  Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification , 2005, ArXiv.

[4]  Pierluigi Maponi,et al.  Image Processing and Retinopathy: A Novel Approach to Computer Driven Tracing of Vessel Network , 2004, ICCSA.

[5]  C. Stewart Retinal Vessel Extraction Using Multiscale Matched Filters, Confidence and Edge Me , 2006 .

[6]  Xiaoyi Jiang,et al.  Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[8]  T. Teng,et al.  Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy , 2006, Medical and Biological Engineering and Computing.

[9]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.