Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.
Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p < 0.0001) with distinct mean +/- SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.
A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining andencoding the Daubechies wavelet decomposed center–neighbour pixel relationshipin the local texture. RDbW features are applied in spatial alignment(registration) of multimodal medical images using a Procrustes analysis(PA)‐based affine transformation function and the registered images are furtherfused by employing a wavelet‐based fusion method. A significant amount ofexperiments is conducted and the registration and fusion accuracy of theproposed feature descriptor is compared with the prominent existing methods suchas local binary patterns (LBP), local tetra pattern (LTrP), local diagonalextrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP).Experimental results show the present registration method improves the averageregistration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP,and LBP, respectively. Further, the fusion results of the current approachexhibit an average improvement in entropy by 11%, standard deviation by 6% edgestrength by 12%, sharpness by 23%, and average gradient by 16% when comparedwith all other feature descriptors used for registering the images. Conceptspresented here can be used widely in analysing the combined information presentin multimodal medical images.
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