Automatic detection of microaneurysms using microstructure and wavelet methods

Abstract

Retinal microaneurysm is one of the earliest signs in diabetic retinopathy diagnosis. This paper has developed an approach to automate the detection of microaneurysms using wavelet-based Gaussian mixture model and microstructure texture feature extraction. First, the green channel of the colour retinal fundus image is extracted and pre-processed using various enhancement techniques such as bottom-hat filtering and gamma correction. Second, microstructures are extracted as Gaussian profiles in wavelet domain using the three-level generative model. Multi-scale Gaussian kernels are obtained and histogram-based features are extracted from the best kernel. Using the Markov Chain Monte Carlo method, microaneurysms are classified using the optimal feature set. The proposed approach is experimented with DIARETDB0 and DIARETDB1 datasets using a classifier based on multilayer perceptron procedure. For DIARETDB0 dataset, the proposed algorithm obtains the results with a sensitivity of 98.32 and specificity of 97.59. In the case of DIARETDB1 dataset, the sensitivity and specificity of 98.91 and 97.65 have been achieved. The accuracies achieved by the proposed algorithm are 97.86 and 98.33 using DIARETDB0 and DIARETDB1 datasets respectively. Based on ground truth validation, good segmentation results are achieved when compared to existing algorithms such as local relative entropy-based thresholding, inverse adaptive surface thresholding, inverse segmentation method, and dark object segmentation.