Automatic extraction of blood vessels in the retinal vascular tree using multiscale medialness.

We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering in the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale line-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale.

Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography.

Aorta dissection is a serious vascular disease produced by a rupture of the tunica intima of the vessel wall that can be lethal to the patient. The related diagnosis is strongly based on images, where the multi-detector CT is the most generally used modality. We aim at developing a semi-automatic segmentation tool for aorta dissections, which will isolate the dissection (or flap) from the rest of the vascular structure.

Evaluation of a semi-automatic segmentation algorithm in 3D intraoperative ultrasound brain angiography.

In this work, we adapted a semi-automatic segmentation algorithm for vascular structures to extract cerebral blood vessels in the 3D intraoperative contrast-enhanced ultrasound angiographic (3D-iUSA) data of the brain. We quantitatively evaluated the segmentation method with a physical vascular phantom. The geometrical features of the segmentation model generated by the algorithm were compared with the theoretical tube values and manual delineations provided by observers.

AMILab software: medical image analysis, processing and visualization.

AMILab is a free software for image analysis, processing and visualization. It provides convenient visualization tools for 2D and 3D images and it is highly extensible through its own scripting language. We describe the main visualization features and the scripting language of AMILab.

Maximal curvature-based segmentation of 3D vessel contours.

The segmentation of three-dimensional vascular trees is an important topic in medical image processing. Although it may seem to be an easy task, many different techniques have been proposed in the literature during the last decade and many difficulties remain. One can wonder why the human eye is usually able to understand the connectivity and the topology of the different structures while most algorithms fail to do so.