Retinal vessel segmentation algorithms are important in automatic retinal disease screening systems. Images captured from fundus camera are useful in screening various diseases affecting retina by processing the images for better diagnosis and planning of treatment. Ophthalmology is a branch of biomedical field which requires computer aided automated techniques for pathology identification in h...

The ability to detect and measure non-calcified plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This is a particularly challenging problem in computed tomography angiography (CTA) imagery because plaques may have similar appearance to nearby blood and muscle tissue. This paper presents an effective technique for automatically detecting soft plaque...

: Advancements in image processing and computing has lead to its use in various applications in medical imaging. In the field of ophthalmology, retinal analysis is used for medical diagnosis and detection of the diseases. Various diseases such as diabetic retinopathy, age-related macular degeneration, muscular dystrophy can be easily diagnosed using automated diagnostic systems, thus providing ...

An automated choroidal vessel segmentation and quantification method for high-penetration optical coherence tomography (OCT) was developed for advanced visualization and evaluation of the choroidal vasculature. This method uses scattering OCT volumes for the segmentation of choroidal vessels by using a multi-scale adaptive threshold. The segmented choroidal vessels are then processed by multi-s...

An accurate segmentation of vascular systems is fundamental for many medical applications. Stability against different contrast levels and noise are very important. In this paper we propose an approach for the segmentation of the vascular system of the liver. It is based on the gradient vector flow (GVF) and Frangis vesselness measure. This method avoids multi-scale analysis and related scale s...

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enh...

Many vascular clinical applications require a vessel segmentation process that is able to extract both the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithm from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detects ...

One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ,...

Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test o...

The segmentation of retinal blood vessels in the retina is a critical step in diagnosis of diabetic retinopathy. In this paper, we present a new method for automatically segmenting blood vessels in retinal images. Five basic algorithms for segmenting retinal blood vessels, based on different image processing techniques, are described and their strengths and weaknesses are compared. A hybrid alg...