{'fraction_non_alphanumeric': 0.014948859166011016, 'fraction_numerical': 0.0, 'mean_word_length': 5.235294117647059, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Complimentary Method of Detection of Glaucoma based on ROI Pre-Processing and Vessel Segmentation

Glaucoma is one of the most common causes of eye blindness and is becoming more important considering the ageing society. In this paper, the research is focused on novel automated classification system for Glaucoma, based on image features from eye fundus photographs. A new data-driven approach is developed which requires no manual supervision. Our goal is to establish a screening system that allows fast, robust and automated detection of Glaucomatous changes in the eye fundus. A study done already has revealed that the juxtapapillary diameters of the retinal vessels such as superior temporal and inferior temporal artery and vein have been shown to be significantly smaller in Glaucomatous eyes than in normal eyes. This aspect has been used by us for the detection of possible Glaucoma. Firstly, disease independent variations, such as non-uniform illumination, size differences are eliminated from the image of the fundus. Region of Interest around optic disc which contains the mentioned vessels is extracted. Simple vessel segmentation strategy is used for vessel detection. Vessel threshold comparator algorithm predicts the possible presence of the Glaucoma.