Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

Abstract

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Current state and future prospects of artificial intelligence in ophthalmology: a review

“…63 Another looked at contact lenses for non-invasively detecting Staphylococcus aureus. 64 Other studies have examined at ophthalmological conditions including cataracts [65][66][67][68] and retinopathy of prematurity 69 whilst some research is focused on improving AI technology for future use, for example, improving AI recognition of the optic nerve head, 70 or improving the false positive or negative rates from imbalanced datasets. 71 Interestingly, some studies are using the ophthalmic datasets for utilization across other aspects of health.…”

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

“…Many different approaches to segmenting of the OD and/or OC in fundus images have been proposed in the literature. The existing methods for automated OD and OC segmentation in fundus images can be broadly classified into three main categories: shape-based template matching [3][4][5][6][7][8][9], active contours and deformable based models [10][11][12][13][14][15][16][17][18], and more recently, machine and deep learning methods [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. We give a brief overview of the existing methods below.…”

“…(c) Machine-and deep-learning methods: Machine learning, and in particular more recent deep learning based methods have shown promising results for OD and OC segmentation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The machine learning based approaches [19][20][21][22][23][24][25][26][27] highly depend on the type of extracted features which might be representative of a particular dataset but not of others. Also, extracting the features manually by hand is a tedious task and takes a considerable amount of time.…”

Development of a hybrid framework to characterize red lesions for early detection of diabetic retinopathy

“…al. [12] proposed a technique for the identification of optic disk that included image-processing, key-point extraction, texture-analysis, visual-dictionary, and classifier techniques. Accuracy of the method was found to be around 94%.…”