Cholesterol Detection Through Iris Using Daugman’s and GLCM Based on K-Means Clustering

Cholesterol is a disease that is influenced by fat deposits originating from the liver. Detection of cholesterol disease can be known through blood tests, urine checks and visually the iris of the human eye. Cholesterol detection through the iris can be implemented using image processing techniques, especially in image segmentation. Input-based image segmentation on feature extraction and pattern classification has been applied in this article. GLCM is a feature extraction technique that is commonly used to sharpen image textures to make the classification process easier. In this article, K-Means have been selected to carry out the classification process. To improve accuracy, the original image has been preprocessed using grayscalling, noise removal, image contrast enhancement and cropping. The experimental results have obtained 100% accuracy.