Manual microaneurysm detection support with size- and shape-based detection

The goal of this paper is to detect visible microaneurysms in retina using size and shape. To achieve the desired goal multi-stage image processing techniques are used. The vascular network and the red regions are highlighted using morphological methods, so these can be segmented with region growing algorithms. Mask is generated that contains only the blood vessels. The red blobs of original images are classified using the generated binary image and their produced skeleton. Our goal is to detect as many as possible microaneurysms taking their shape and size into account. Experiments with manual settings on the test images showed approx. 50% of detection performance, and it can be further improved by development of pre-processing and increasing classification criteria. The developed system can help the manual evaluation of microaneurysms, hence can accelerate of the work of doctors.