Retinal Microaneurysms Detection using Local Convergence Index Features
This work addresses the problem of early blindness prevention in diabetic patients through improved medical image analysis, though it appears incremental with a focus on feature and classifier enhancements.
The paper tackles automatic detection of retinal microaneurysms for early diabetic retinopathy diagnosis by proposing a method using local convergence index features and a hybrid classifier, achieving an average sensitivity of 0.471 on the ROC dataset and outperforming state-of-the-art approaches.
Retinal microaneurysms are the earliest clinical sign of diabetic retinopathy disease. Detection of microaneurysms is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of microaneurysms in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (RGB and SLO) using five publicly available datasets including the Retinopathy Online Challenge's dataset. The proposed method achieves an average sensitivity score of 0.471 on the ROC dataset outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other four datasets demonstrate the effectiveness and robustness of the proposed microaneurysms detection method regardless of different image resolutions and modalities.