  All people with diabetes have the risk of developing diabetic retinopathy
(DR), a vision-threatening complication. Early detection and timely treatment
can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has
the potential benefit of improving the accuracy and speed in DR detection. This
study is concerned with automatic classification of images with microaneurysm
(MA) and neovascularization (NV), two important DR clinical findings. Together
with normal images, this presents a 3-class classification problem. We propose
a modified color auto-correlogram feature (AutoCC) with low dimensionality that
is spectrally tuned towards DR images. Recognizing the fact that the images
with or without MA or NV are generally different only in small, localized
regions, we propose to employ a multi-class, multiple-instance learning
framework for performing the classification task using the proposed feature.
Extensive experiments including comparison with a few state-of-art image
classification approaches have been performed and the results suggest that the
proposed approach is promising as it outperforms other methods by a large
margin.
