An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters

Abstract

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.

Formal and Model Driven Design of the Bright Light Therapy System Luxamet

“…For example, breast imaging for cancer detection relies on sophisticated image processing algorithms [60,61]. Similarly, Computer-Aided Diagnosis (CAD) systems for plaque [62][63][64][65], cardiac disease [66,67] and diabetes [68,69] relay also heavily on computerized processing. Hence, these CAD systems stand to benefit from formal and model driven biomedical systems design, because the design methodology helps us to realize systemic safety and reliability.…”

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

“…All related texture features from the statistical-based texture analysis were generated from each retinal image that derived from gray level co-occurrence matrix (GLCM) and run-length matrix [17][18][19][20][21][22][23][24][25].…”

“…Other similar approach can also be used: having the GLCM normalized, we can then derive eight second order statistic features which are also known as haralick features [22] for each image, which are: contrast, correlation, energy, entropy, homogeneity, dissimilarity, inverse difference momentum, maximum probability. In addition to these features, we also applied correlation, dissimilarity, inverse difference momentum and maximum probability, which is different from above mentioned features.…”

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

“…Frame [45] applied statistical texture measures, calculated using the grey level co-occurrence matrix (GLCM), to identify irregular distributions of pixel intensities associated with neovascularisation. Acharya [46] calculated texture features from the run length matrix, as well as the GLCM to identify the stage of DR. Multi-scale amplitude modulation frequency modulation (AM-FM) methods were utilised by Agurto [47] for spectral texture analysis to characterise different retinal structures, including new vessels. However, Agurto [48] extended their work to involve AM-FM along with vessel segmentation and granulometry to detect NVD.…”