Automatic diagnosis of diabetic retinopathy from fundus images using digital signal and image processing techniques

Automatic diagnosis and display of diabetic retinopathy from images of retina using the techniques of digital signal and image processing is presented in this paper. The acquired images undergo pre-processing to equalize uneven illumination associated with the acquired fundus images. This stage also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the abnormalities detection stage was used to distinguish between candidate lesions and other information. Methods of diagnosis of red spots, bleeding and detection of vein-artery crossover points have also been developed in this work using the color information, shape, size, object length to breadth ration as contained in the acquired digital fundus image. The algorithm was tested with a separate set of 25 fundus images. From this, the result obtained for Microaneurysms and Haemorrhages diagnosis shows the appropriateness of the method

Automatic diagnosis of diabetic retinopathy from fundus images using digital signal and image processing techniques

Automatic diagnosis and display of diabetic retinopathy from images of retina using the techniques of digital signal and image processing is presented in this paper. The acquired images undergo pre-processing to equalize uneven illumination associated with the acquired fundus images. This stage also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the abnormalities detection stage was used to distinguish between candidate lesions and other information. Methods of diagnosis of red spots, bleeding and detection of vein-artery crossover points have also been developed in this work using the color information, shape, size, object length to breadth ration as contained in the acquired digital fundus image. The algorithm was tested with a separate set of 25 fundus images. From this, the result obtained for Microaneurysms and Haemorrhages diagnosis shows the appropriateness of the method

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

Implementasi segmentasi lesi merah pada citra fundus retina mata berwarna menggunakan pendekatan morfologi

Mata manusia terdiri dari banyak susunan komponen yang dapat memberikan informasi mengenai kondisi tubuh kita. Salah satu penyakit yang memiliki dampak besar kepada penglihatan manusia adalah retinopati diabetes. Retinopati diabetes menyebabkan pendarahan pada retina yang dapat menyebabkan kebutaan permanen. Pendeteksian manual terhadap pendarahan atau lesi merah pada retina cukup sulit dilakukan karena penampakan atribut pada citra fuundus mata berwarna cukup kompleks. Sehingga dibutuhkan adanya sistem yang dapat secara otomatis dan akurat dalam melakukan segmentasi terhadap lesi merah (microaneurysm dan hemorrhage) pada citra fundus retina mata berwarna. Pada Tugas Akhir ini, metode yang akan diimplementasikan dalam proses segmentasi lesi merah pada citra fundus retina mata berwarna adalah matematika morfologi. Terdapat tiga tahap dalam Tugas Akhir ini. Tahap pertama adalah preprocessing dengan mengekstraksi Green channel dari ruang warna citra RGB dan mengimplementasikan algoritma Contrast Limited Adaptive Histogram Equalization (CLAHE). Tahap kedua adalah segmentasi dengan metode matematika morfologi dan tahap terakhir adalah perbaikan segmentasi. Uji coba pada Tugas Akhir ini menggunakan Citra fundus retina mata berwarna pada dataset DIARETDB1 yang terdiri dari 89 foto retina yang diambil dengan 50 derajat kamera fundus digital. Dengan menggunakan dataset ini, didapatkan rata rata akurasi 99,22%, sensitivitas 81,32 % dan spesifisitas 99,59% pada 25 kali percobaan. Metode tersebut terbukti dapat mensegmentasi lesi merah pada citra fundus retina mata berwarna dengan baik. ===================================================================================================== Human eyes consist of many structured components that can give information about condition of our body. One of the diseases that has a huge impact on human’s vision is diabetic retinopathy. Diabetic retinopathy causes hemorrhaging in retina that can cause permanent blindness. Manual detection concerning hemorrhages or red lesions on retina is hard enough due to the complexity of component on colored eye retinal fundus images. Therefore, it is required the existence of a system that can automatically and accurately do the detection of red lesions (microaneurysm and hemorrhages) on colored eye retinal fundus images. In this Final Project, the method that will be implemented in the process of segmentation of red lesions on the colored eye retinal fundus images is mathematical morphology. There are three steps in this Final Project. The first step is to do the preprocessing by extracting green channel from RGB color space and implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The Second step is implementing the segmentation by mathematical morphology method and the last step is perbaikan segmentasi. Experiments in this Final Project use colored eye retinal fundus images taken from DIARETDB1 dataset which consists of 89 retinal image that captured by 50 degrees field of view digital fundus camera. By using this dataset for 25 times of experiments, obtained these following results; the average accuracy is 99,22%, the sensitivity is 81,32% and the spesificity is 99,59% . This method is proven to be able to do the segmentation on red lesions of colored eye retinal fundus images

The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging.

Purpose:High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging. Methods:The system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity. Results:The system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%. Conclusions:Automation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening. Translational Relevance:Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening

Joint segmentation and classification of retinal arteries/veins from fundus images

Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree. Results The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%. Conclusion The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest. Significance The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin