Vascular Tree Extraction Research Articles

A Comparative Study of Different Blood Vessel Detection on Retinal Images

Detection of blood vessel plays an important stage in different medical areas, such as ophthalmology, oncology, neurosurgery, and laryngology. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, several local segmentation techniques which include such as Vascular Tree Extraction, Tyler L. Coye and Line tracking, Kirsch’s Template and Fuzzy C Mean methods were studied. The main objective is to determine the best approaches in order to detect the blood vessel on the degraded retinal input image (DRIVE dataset). A few Image Quality Assessment (IQA) was obtained to prove the effectiveness of each detection methods. Overall, the result of sensitivity highest came from Kirsch Templates (96.928), while specificity from Fuzzy C means (77.573). However, in term of accuracy average, the Line Tracking method is more successful compared to the other methods.

Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model

Background and ObjectiveRetinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. MethodsIn this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. ResultsThe performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. ConclusionsThis technique results in a classifier with high precision and recall when comparing it with Xu’s method.

Front Cover: Vascular tree extraction for photoacoustic microscopy and imaging of cat primary visual cortex (J. Biophotonics 6‐7/2017)

Front Cover: Vascular tree extraction for photoacoustic microscopy and imaging of cat primary visual cortex (J. Biophotonics 6‐7/2017)

Vascular tree extraction for photoacoustic microscopy and imaging of cat primary visual cortex.

A vascular tree extraction algorithm is proposed to automatically extract independent and complete vascular trees from both background and other crossed vascular trees for photoacoustic microscopy (PAM) imaging. Extracted parameters include vascular tree centerline, diameters, boundaries and three-dimensional (3-D) direction along the tree. Based on the concept of blood vessel tracking, the proposed algorithm extracts complete vascular trees by utilizing a ray casting framework to realize functions which includes vessel direction estimation, vessel branching detection and vessel crossover point detection. An optical-resolution PAM (OR-PAM) system is set up and the acquired images of cat primary visual cortex are used to demonstrate the effectiveness of the proposed algorithm. The proposed algorithm successfully extracts a complete and complex arteriole tree composed of multiple loop structures. Most branches and vessel crossovers in the arteriole tree are accurately extracted. Accuray of the algorithm is further tested on phantom images and real OR-PAM vascular tree images. As the extracted parameters are directly related with monitoring hemodynamic responses at the level of vascular trees, the proposed algorithm may facilitate the application of PAM on studies of neurovascular coupling and related brain functions and diseases. OR-PAM maximum intensity projection image of cat primary visual cortex.

Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images.

Morphological Multiscale Enhancement, Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram

This paper presented an automatic morphological method to extract a vascular tree using an angiogram. Under the assumption that vessels are connected in a local linear pattern in a noisy environment, the algorithm decomposes the vessel extraction problem into several consecutive morphological operators, aiming to characterize and distinguish different patterns on the angiogram: background, approximate vessel region and the boundary. It started with a contrast enhancement and background suppression process implemented by subtracting the background from the original angiogram. The background was estimated using multiscale morphology opening operators by varying the size of structuring element on each pixel. Subsequently, the algorithm simplified the enhanced angiogram with a combined fuzzy morphological opening operation, with linear rotating structuring element, in order to fit the vessel pattern. This filtering process was then followed by simply setting a threshold to produce approximate vessel region. Finally, the vessel boundaries were detected using watershed techniques with the obtained approximate vessel centerline, thinned result of the obtained vessel region, as prior marker for vessel structure. Experimental results using clinical digitized vascular angiogram and some comparative performance of the proposed algorithm were reported.

DETECTION OF BLOOD VESSELS FROM RETINAL IMAGES USING WATERSHED TRANSFORMATION

Automated analysis and interpretation of retinal images has become an incontournable diagnostic step in ophthalmology. Retinal blood vessels morphology can be an important indicator for diseases such as diabetic retinopathy; and their detection also serves for image registration. This paper presents a method based on mathematical morphology for extraction of vascular tree in color retinal image with low contrast. It consists in contrast enhancement and application of watershed transformation in order to segment blood vessels in digital fundus images.