Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017

Automatic Discrimination of Color Retinal Images using the Bag of Words Approach

International audienceDiabetic retinopathy (DR) and age related macular degeneration (ARMD) are among the major causes of visual impairment all over the world. DR is mainly characterized by small red spots, namely microaneurysms and bright lesions, specifically exudates. However, ARMD is mainly identified by tiny yellow or white deposits called drusen. Since exudates might be the only visible signs of the early diabetic retinopathy, there is an increase demand for automatic diagnosis of retinopathy. Exudates and drusen may share similar appearances; as a result discriminating between them plays a key role in improving screening performance. In this research, we investigative the role of bag of words approach in the automatic diagnosis of retinopathy diabetes. Initially, the color retinal images are preprocessed in order to reduce the intra and inter patient variability. Subsequently, SURF (Speeded up Robust Features), HOG (Histogram of Oriented Gradients), and LBP (Local Binary Patterns) descriptors are extracted. We proposed to use single-based and multiple-based methods to construct the visual dictionary by combining the histogram of word occurrences from each dictionary and building a single histogram. Finally, this histogram representation is fed into a support vector machine with linear kernel for classification. The introduced approach is evaluated for automatic diagnosis of normal and abnormal color retinal images with bright lesions such as drusen and exudates. This approach has been implemented on 430 color retinal images, including six publicly available datasets, in addition to one local dataset. The mean accuracies achieved are 97.2% and 99.77% for single-based and multiple-based dictionaries respectively

Bright Lesion Detection in Color Fundus Images Based on Texture Features

In this paper a computer aided screening system for the detection of bright lesions or exudates using color fundus images is proposed. The proposed screening system is used to identify the suspicious regions for bright lesions. A texture feature extraction method is also demonstrated to describe the characteristics of region of interest. In final stage the normal and abnormal images are classified using Support vector machine classifier. Our proposed system obtained the effective detection performance compared to some of the state–of–art methods

Automated Retinal Lesion Detection via Image Saliency Analysis

Background and objective:The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods :Retinal images are firstly segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low-rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disc, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at pixel-level from different modalities of retinal images, without the need to tune parameters. Results:To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at pixel-level, lesion-level, or image-level according to ground truth availability in these datasets. Conclusions:The experimental results show that the proposed method outperforms existing state-of-the-art ones in applicability, effectiveness, and accuracy

Detection of microaneurysms in retinal images using an ensemble classifier

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

[EN] Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. 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