Enhancement of Coronary Blood Vessels based on Frangi’s Vesselness Filter and Morphological Operations

Cardiovascular diseases (CVDs) are the global cause of deaths and therefore research in modern medical image processing aims to develop a medical tools to assist the clinicians in vessel extraction, artery detection and 3D reconstruction. Vessel extraction is an important and trivial step which depends extremely on enhancement method. Extraction of coronary artery blood vessels from 3 Dimension (3D) Coronary Computed Tomography Angiography (CCTA) images is a demanding research objective to strengthen the diagnosis and therapy of coronary artery illness. This paper presents a vessel enhancement method of coronary artery blood vessels using Frangi’s vesselness measure and morphological operators. In the first stage of the proposed work, Preprocessing is performed to consider only the heart region. Next Frangi’s vesselness measure is calculated for the 3D CCTA images. While calculating the Frangi’s vesselness measure, four different types of gradient operators are used for calculating the Hessian matrix viz., Sobel, Prewitt, central difference and intermediate difference operators. In the second stage, the vessels are enhanced by morphological operations based on top hat and bottom hat operations. These morphological operations help in further enhancing the blood vessels. The proposed methodology was applied on 12 3D CCTA dataset and evaluated using quality measures such as MSE, PSNR, SSIM and FSIM. The results obtained based on the four gradient operators are compared. The statistical test viz., one way ANOVA was carried out on the results. The proposed method using Prewitt operator is able to extract even small vessels and the results seem to be promising.

An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images

In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.

Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.

Automatic Detection of Blood Vessel in Retinal Images Using Vesselness Enhancement Filter and Adaptive Thresholding

Retinal blood vessels detection and measurement of morphological attributes, such as length, width, sinuosity and corners are very much important for the diagnosis and treatment of different ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. This paper presents a integration method for blood vessels detection in fundus retinal images. The proposed method consists of two main steps. The first step is pre-processing of retinal image to improve the retinal images by evaluation of several image enhancement techniques. The second step is vessels detection, the vesselness filter is usually used to enhance the blood vessels. The enhancement filter is designed from the adaptive thresholding of the output of the vesselness filter for vessels detection. The algorithms performance is compared and analyzed on three publicly available databases (DRIVE, STARE and CHASE_DB) of retinal images using a number of measures, which include accuracy, sensitivity, and specificity.