Feature extraction from retina vascular images for classification

Classifying medical images is tedious and complex task. Using machine learning algorithms to assist the process could be a huge help. There are many challenges to make machine learning algorithms work reliably on image data. First of all you need rather large image database with ground truth information (expert’s labeled data with diagnosis information). Second problem is preprocessing images, including merging modalities, unifying color maps, normalizing and filtering. This part is important and may impact last part – feature extraction. This step is crucial, because on how well you are able to extract informative features, depends how well machine learning algorithms will work. Dataset In order to demonstrate classification procedure of medical images the ophthalmology STARE (STructured Analysis of the Retina) image database was pulled from http://cecas.clemson.edu/~ahoover/stare/. The database consist of 400 images with 13 diagnostic cases along with preprocessed images. For classification problem we have chosen only vessels images…

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