@conference{omia 27647, author = {Ivo Soares, Miguel Castelo-Branco, Antonio M.G. Pinheiro}, title = {Microaneurysms detection using a novel neighborhood analysis}, volume = {1}, year = {2014}, url = {https://pubs.lib.uiowa.edu/omia/article/id/27647/}, issue = {2014}, doi = {10.17077/omia.1010}, abstract = {<p>The earliest sign of the diabetic retinopathy is the appearance of small red dots in retinal fundus images, designated by microaneurysms. In this paper a scale-space based method is proposed for the microaneurysms detection. Initially, the method performs a segmentation of the retinal vasculature and defines a global set of microaneurysms candidates, using both coarser and finer scales. Using the finer scales, a set of microaneurysms candidates are analysed in terms of shape and size. Then, a set of gaussian-shaped matched filters are used to reduce the number of false microaneurysms candidates. Each candidate is labeled as a true microaneurysm using a new neighborhood analysis method. The proposed algorithm was tested with the training Retinopathy Online Challenge (ROC) dataset, revealing a 47% Sensitivity with an average number of 37.9 false positives per image.</p>}, month = {9}, pages = {65-72}, publisher={University of Iowa}, journal = {Proceedings of the Ophthalmic Medical Image Analysis International Workshop} }