M2U-net: Effective and efficient retinal vessel segmentation for real-world applications

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)

Review on Optic Disc Localization Techniques

The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss

Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach

Diabetic Retinopathy is a retinal vascular disease that is characterized by progressive deterioration of blood vessels in the retina and is distinguished by the appearance of different types of clinical lesions like microaneurysms, hemorrhages, exudates etc. Automated detection of the lesions plays significant role for early diagnosis by enabling medication for the treatment of severe eye diseases preventing visual loss. Extraction of blood vessels can facilitate ophthalmic services by automating computer aided screening of fundus images. This paper presents blood vessel extraction algorithms with ensemble of pre-processing and post-processing steps which enhance the image quality for better analysis of retinal images for automated detection. Extensive performance based evaluation of the proposed approaches is done over four databases on the basis of statistical parameters. Comparison of both blood vessel extraction techniques on different databases reveals that fuzzy based approach gives better results as compared to Kirsch’s based algorithm. The results obtained from this study reveal that 89% average accuracy is offered by the proposed MBVEKA and 98% for proposed BVEFA