distribution of retinal blood vessels (rbvs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. therefore, detection of the exact location of rbvs is very important for ophthalmologists. one of the frequently used techniques for extraction of these vessels is region growing-based se...

Angiography (i.e. vessel imaging after the injection of a radiopaque substance) is a widely used procedure for vessel observation in both clinical routine and medical research. Often for the subsequent analysis of the vasculature it is needed to measure the angiogram area covered by vessels and/or the vessel length. For this purpose we need vessel enhancement and segmentation. While there are a...

In this work we formulate vessel segmentation on contrast-enhanced CT angiogram images as a Bayesian tracking problem. To obtain posterior probability estimation of vessel location, we employ sequential Monte Carlo tracking and propose a new vessel segmentation method by fusing multiple cues extracted from CT images. These cues include intensity, vesselness, organ detection, and bridge informat...

Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline...

An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic ves...

BACKGROUND Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. METHODS This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for ves...

Vessel analysis is important for a wide range of clinical diagnoses and disease research such as diabetes and malignant brain tumours. Vessel segmentation is a crucial first step in such analysis but is often complicated by structural diversity and pathology. Existing automated techniques have mixed results and difficulties with non-idealities such as imaging artifacts, tiny vessel structures a...

Diabetic Retinopathy is the most common cause of blindness in the working population of the western world and is very common among people who suffer from diabetes. Fortunately, during a clinical examination an ophthalmologist is able to determine the onset of the disease by taking certain features of the retinal vessels of the fundus into account. These features include the narrowing of vessels...

Vessel segmentation is one of the challenging tasks in the field of image analysis. This paper gives a comprehensive study for liver vessel segmentation of 3D images. The main goal is to segment the liver vessels in a given 3D DICOM image of the abdomen. We proposed a cascaded liver segmentation structure such that we first extracted the liver region from the abdomen, then vessel enhancement is...

Blood vessel segmentation from the retinal images is extremely important for assessing retinal abnormalities. A good amount of research has been reported on blood vessel segmentation, but significant improvement is still a necessity particularly on minor vessel segmentation. As the local contrast of blood vessels is unstable (intensity variation), especially in unhealthy retinal images, it beco...