Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Abstract

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

“…Fewer ARIA investigations have been devoted to the automated detection and classification of images of age-related macular degeneration [3,11,18,28,32], and there is a relative paucity of image analysis studies dedicated specifically to automated GA characterization.…”

Local configuration pattern features for age-related macular degeneration characterization and classification

“…Automatic segmentation of drusen and its measurement is needed to automate the diagnostic process [14]. Hence, several authors have proposed AMD detection using automated drusen segmentation [15][16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Their method reported an AUC of 0.84 and 0.77 using RIST and UTHSCSA respectively. Texture and DWT based features are used in [14,33] to discriminate normal and AMD classes. Their method yielded an accuracy of 93.70% and 95.07% respectively.…”

“…Their method yielded an accuracy of 93.70% and 95.07% respectively. Important limitations of the aforementioned methods except [14,33] requires removal of OD, blood vessel and macula detection, before feature extraction. The difference between the reported works in the literature and proposed work in this paper is (a) anomaly and normal anatomy of fundus images such as drusen, OD and blood vessel segmentation are not needed, and (b) this work uses a less number of features to obtain highest performance measures compare to all reported works [11,14,30,33].…”

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

“…Farsiu et al assessed the thickness of human retinal layers to discriminate AMD patients from the normal controls [9]. The distributions of grayscale features in the digital fundus images may also be integrated with the entropies and higher order spectra to detect the AMD samples [18]. By using five features analyzed from thickness profiles and cyst fluids, Hassan et al built a model to classify macular edema and central serous retinopathy (CSR).…”