Abstract
In the process of automatic Glaucoma diagnosis, we have used freely available database for research work, like DRIONS-DB, RIM-ONE, MESSIDORE (Base 1 to Base 12), DRISHTY, HRF (High Resolution Fundus Images) total 2866 retinal fundus images we have used for evaluation of Intra ocular pressure using different image segmentation techniques, like HAAR wavelet, median filter, morphological opening, and top-hat filters, from these techniques first we have extracted features important to diagnose Glaucoma like retinal blood vessels with the fine features of arteries, capillaries and veins. After extracting features we have calculated statistical features important to diagnose glaucoma like area, diameter, length, thickness and tortuosity to measure the intra ocular pressure generated in retinal blood vessels, all these procedures are divided in to two separate experiments. Performed statistical calculations and feature extraction separately then in advance procedure of diagnosing we have applied K-Means calcification and clustering methods separately on both the experiments to measure the intensity of disease. Then on the basis of comparison, we have concluded that Top-hat filter method or experiment number two gives better result than another one, overall we got highest 85.
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Acknowledgement
We would like to acknowledge all the medical dataset provider agencies for research purpose, like DRIONS-DB, DRISHTI, MESSIDORE, RIN-ONE and HRF. If the learner wanted to implement and develop their own graphical user interface (GUI), then please kindly refer, “Understanding Programming Aspects of Pattern Recognition Using MATLAB”, and the same kind of experiments also available in “Glaucoma Diagnosis in the Vision of Biomedical Image Analysis” and “Projects in digital image processing”, By the same authors Dr. Ramesh R. Manza & Dr. Dnyaneshwari D. Patil, Shroff Publisher & Distributer Pvt. Ltd.
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Patil, D.D., Manza, R.R., Ramteke, R.J., Rajput, Y., Harke, S. (2019). Comparison with Evaluation of Intra Ocular Pressure Using Different Segmentation Techniques for Glaucoma Diagnosis. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_2
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DOI: https://doi.org/10.1007/978-981-13-9184-2_2
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