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Abstract--The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving [...]

Glaucoma and diabetic retinopathy are the most common eye diseases and the leading cause of blindness around the world. The prime objective of this study is to devise and develop an experimental computer‐aided diagnosis system to provide an efficient way for assisting the ophthalmologist in early detection of ocular diseases such as glaucoma and diabetic retinopathy. The proposed technique follows three stages: Pre‐processing, feature selection and classification. Initially, the fundus image is pre‐processed to extract the green channel image, and the obtained green channel image is further enhanced using contrast limited adaptive histogram equalisation technique. Three different kinds of features: Clinical features, transform domain features and structural features are utilised to extract the relevant information from the enhanced fundus images. To avoid redundant information, an improved feature selection mechanism is used to select the optimum set of features from the extracted features. Subsequently, the selected features are used to train the support vector machine classifier for the classification of the retinal diseases with 10‐fold cross‐validation. The performance of the proposed method is assessed using eight different quantitative evaluation measures. The experimental results demonstrate the effectiveness of the proposed work over prior works for the early detection of ocular diseases. [ABSTRACT FROM AUTHOR]

In this article, an attempt is made to combine both transform and spatial domain features to increase the accuracy, sensitivity, and specificity for the classification of retinal diseases. Papilloedema, macular edema, glaucoma, diabetic retinopathy (DR), and central retinal vein occlusion (CRVO) are the leading cause of blindness in humans. To identify these diseases in a mass screening process, it consumes intense labor and time. In this proposed work, an automated computer aided algorithm is developed to classify the normal from the abnormal (Papilledema, macular edema, glaucoma, DR, and CRVO) images. For any classification task, an increase in data size, number of classes, and dimension of the feature space affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem. In the proposed algorithm, the feature fusion technique is employed to reduce the input data to the classifier. The features are extracted from wavelet packet transform (WPT) and intensity hue saturation (IHS). A support vector machine (SVM)‐based learning algorithm is used to train the classifier using the extracted features. The proposed method yields better results than the existing classifier fusion method with an overall accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and area under ROC curve (AUC) of 96.3%, 95.8%, 97.8%, 96.4%,98.1%, and 0.971 respectively. [ABSTRACT FROM AUTHOR]

In this paper, an iterative adaptive unsymmetric trimmed shock filter based on partial differential equations (PDE) is proposed to remove high-density salt-and-pepper noise by preserving the edge details in the images. This algorithm consists of two steps: identification of pixels affected by the salt-and-pepper noise and recovery of these noisy pixels using adaptive unsymmetric trimmed shock filter. Shock filter has been used in image processing literature for image restoration and enhancement task. However, its use was limited to unwanted noise removal, enhancement, despeckling, etc. In the proposed work, a modified form of shock filter termed as adaptive unsymmetric trimmed shock filter is designed to remove the salt-and-pepper noise. The proposed algorithm is tested with different test images. Three performance evaluation measures: PSNR, CR and MSSIM are used to validate the proposed scheme. The performance of the proposed algorithm claims better results than the other considered eight existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]