Segmentation of Retinal Blood Vessels Using U-Net++ Architecture and Disease Prediction

Direction-Based Segmentation of Retinal Blood Vessels

Automatic segmentation of glioma tumors from BraTS 2018 challenge dataset using a 2D U-Net network

Background: Glioma is the most common primary brain tumor, and early detection of tumors is important in the treatment planning for the patient. The precise segmentation of the tumor and intratumoral areas on the MRI by a radiologist is the first step in the diagnosis, which, in addition to the consuming time, can also receive different diagnoses from different physicians. The aim of this study...

Pre-Processing Steps for Segmentation of Retinal Blood Vessels

Segmentation of blood vessels in retinal images is an important part in retinal image analysis for diagnosis and treatment of eye diseases for large screening systems. In this paper, we addressed the problem of background and noise extraction from retinal images. Blood vessels usually have central light reflex and poor local contrast, hence the results yield by blood vessel segmentation algorit...

Segmentation of Retinal Blood Vessels Based on Cake Filter.

Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filte...

Using MAS to Detect Retinal Blood Vessels

The segmentation of retinal vasculature by color fundus images analysis is crucial for several medical diagnostic systems, such as the diabetic retinopathy early diagnosis. Several interesting approaches have been done in this field but the obtained results need to be improved. We propose therefore a new approach based on an organization of agents. This multi-agent approach is preceded by a pre...