Journal article Open Access

Diabetic Retinopathy – Feature Extraction and Classification using Adaptive Super Pixel Algorithm

Balambigai Subramanian; V. Saravanan; Rudra Kalyan Nayak; T. Gunasekaran; S. Hariprasath


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    <subfield code="a">Retinopathy, fundus, adaptive super pixel, classification</subfield>
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    <subfield code="u">Associate Professor, Department of Computer Science and Engineering, K L Deemed to be University, Guntur (Andhra  Pradesh) India</subfield>
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    <subfield code="u">Associate Professor, Department of Computer Science and Engineering, K L Deemed to be University, Guntur (Andhra  Pradesh) India</subfield>
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    <subfield code="u">Assistant Professor in Department of Electronics  and Communication Engineering, Saranathan College of Engineering,  Trichy (Tamil Nadu) India</subfield>
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    <subfield code="u">Associate Professor, Department of  Electronics and Communication Engineering, Kongu Engineering College</subfield>
    <subfield code="a">Balambigai Subramanian</subfield>
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    <subfield code="a">&lt;p&gt;Diabetic Retinopathy is an ocular manifestation of diabetes . The longer a person has diabetes, higher are the chances of having diabetic retinopathy in their visual system. Hence the objective of this research work is to propose an automated, suitable and sophisticated approach using image processing so that diabetic retinopathy can be detected at early levels easily and damage to retina can be minimized. A vital point of diabetic retinopathy that it causes detectable changes in the blood vessels of the retina. The focal blurred edges are detected so as to dismiss the false alarms. A two-level approach is used here to classify data. Firstly, optimal features are extracted from the training data and secondly, the classification is done by the use of the adaptive super pixel algorithm and then the test data is analyzed. Adaptive super pixel algorithm can adjust the weights of various features based on their discriminating ability. After the application of algorithm, the diabetic eye is detected by means of various parameters like colour, texture, spatial distance, contour, mean, standard deviation, entropy and maximum pixel points. This research can aid the doctor for easy detection of the disease as it given an accuracy of about 98.33%.&lt;/p&gt;</subfield>
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