Diagnosis of Cataract using a Robust Algorithm for Telemedicine Applications
Sarthak Khanna1, Abhishek Iyer2, T.K.Sivakumar3
1Sarthak Khanna*, Department of Computer Science and Engineerin, SRM Institute of Science and Technology, Kattankulathur.
2Abhishek Iyer, Student, Bachelor of Technology from SRM Institute of Science and Technology, Kattankulathur.
3Dr.TK Sivakumar, Phd, Bharathiar University, Coimbatore, Tamil nadu.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 40-43 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6432029320/2020©BEIESP | DOI: 10.35940/ijeat.C6432.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This project proposes and evaluates an algorithm to automatically detect cataract from colored images of adult human subjects. The methods currently available make use of DSLR (Digital Single Lens Reflex) cameras which are very expensive thereby, making the whole process expensive. The main objective of this project is to provide a robust and affordable alternative to the classic treatment method which adopts the above methods. In this project via an algorithm we aim to diagnose the presence of cataract from the true colour images of an eye. The algorithm proposed makes use of OpenCV for cataract screening based on textural features such as uniformity, intensity and standard deviation. These features are first computed and mapped using Data Mining techniques after consultation with an eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Pre-processing includes conservative smoothing followed by image de-noising. The isotropic Gaussian filter is widely used as a low pass filter for image de-noising.Feature extraction is done after pre-processing to extract all the information for cataract detection from the eye’s pupil region. The extracted parameters were compared with the values obtained from an ophthalmologist to determine the presence of Cataract in the eye of the patient. Finally, a tele-ophthalmology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.
Keywords: DSLR camera, OpenCV, de-noising, reprocessing.