In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to subset of input image dimensions. this work, we propose novel convolutional neural network architecture that can effectively learn produce lower-dimensional than image. The restores encoded representation only in spatial dimensions and keeps unchanged others. newly proposed projective s...

PURPOSE The retina has been found affected in Parkinson's disease (PD). It is unclear if this is due to neurodegeneration of local dopamine-dependent retinal cells, a result of central nervous degeneration including the optic nerve or retinal small vessel disease. This study aimed to detect changes of the retinal vasculature in PD patients compared to controls. METHODS We examined 49 PD patie...

We propose a multiple scanner vendor registration method for pathological retinal 3D spectral domain optical coherence tomography volumes based on Myronenko’s Coherent Point Drift and our automated vessel shadow segmentation. Coherent point drift is applied to the segmented retinal vessel point sets used as landmarks to generate the registration parameters required. In contrast to other registr...

Enhancement, followed by segmentation, quantification and modelling, of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method, a new approach based on mathematical morphology, for vessel enhancement. The proposed method combines different structuring elements to detect innate features of vesse...

Abnormality detection plays an important role in many real-life applications. Retinal vessel segmentation algorithms are the critical components of circulatory blood vessel Analysis systems for detecting the various abnormalities in retinal images. Traditionally, the vascular network is mapped by hand in a time-consuming process that requires both training and skill. Automating the process allo...

The field of medical imaging gains its importance with increase in the need of accurate and efficient diagnosis over a short period of time. Since manual processes are tedious, time consuming and impractical for large data there is a need for automatic processing that helps community health workers. Retinal blood vessel segmentation in diabetic retinopathy plays an important role in diagnosing ...

Retinal blood vessel structure is an important indicator of many retinal and systemic diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and three unsupervised segmentation methods with a publicly available implementation are reviewed and quantitatively compared with each other on five public databases with gr...

Novel retinal imaging techniques have enabled the assessment of quantitative vascular parameters, which provide information on the microvasculature before the appearance of retinopathy signs. Advances in neuroimaging have revealed that cerebral microbleeds (CMB) - besides lacunar infarcts and white matter lesions (WML) - may be a novel marker of cerebral small vessel disease. We examine whether...

The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so automatic vessel segmentation serves as an essential stage for efficient detection eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential tasks, but have limited feature representation ability. In addition, they don’t effectively con...