Abstract
Computer-aided detection and diagnosis of diabetic retinopathy with retinal fundus images is the necessary step for the implementation of a large scale screening effort in regions where ophthalmologists are not available. In this paper we propose computer-aided binary detector of bright lesions in retinal fundus images. It is based on wavelets for multiresolution feature discrimination and support vector machine (SVM) for classification. After thresholding the sub-band images resulting from the Isotropic Undecimated Wavelet Transform (IUWT) decomposition of the input image, we employ an approach based on the image Hessian eigenvalues and multi-scale image analysis, for designing good feature descriptors of bright lesions. These are afterwards used in the SVM model classifier. Experimental results on our current data set show that the proposed method is efficient and achieves a very good success rate.
This work was partially supported by the project PTDC/MATNAN/0593/2012, and also by CMUC and FCT (Portugal), through European program COMPETE/ FEDER and project PEst-C/MAT/UI0324/2011).
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Figueiredo, I.N., Kumar, S. (2014). Wavelet-Based Computer-Aided Detection of Bright Lesions in Retinal Fundus Images. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_21
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