  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.
