Abstract
In medical imaging practice, vascular enhancement filtering has been widely performed before vessel segmentation and centerline detection, which provides important pathological information and holds great significance for vessel quantification. In the literature, numerous well known vesselness filtering approaches have been developed. For example, some techniques explore the Hessian matrix of the original images and construct the vessel filter based on the eigenvalues of the Hessian matrix. In this work we develop a hybrid technique for fast and accurate vascular enhancement filter, which contains two main steps: vesselness diffusion and improved vesselness filter based on the eigenvalues ratio. This novel approach is quantitatively and qualitatively tested on the public 2D retinal datasets and 3D synthetic vascular structure models. Experimental results demonstrate that the proposed filter outperforms other existing approaches for curvilinear structure enhancement from noisy images. Moreover, the novel approach is further evaluated on real patient Coronary Computed Tomography Angiography (CCTA) datasets with ground truth regions labelled by professional cardiologist. Our method is proven to produce more accurate coronary artery segmentation results. Given the accuracy and efficiency, the proposed vesselness filter can be further used in medical practice for vascular structures enhancement before vessel segmentation and quantification.
Highlights
Vascular structures detection plays an utmost role in various applications of medical image processing and analysis, for example, cardiovascular disease screening [1], [2]
In this study, we developed a hybrid vesselness filter for vascular structures enhancement from noisy medical images, which takes the advantages of vesselness enhancement diffusion, and integrates the improved Frangi’s filter based on the ratio of eigenvalues of the Hessian matrix
The Hessian matrix was computed and the novel vessel enhancing filter was developed based on the eigenvalues ratio
Summary
Vascular structures detection plays an utmost role in various applications of medical image processing and analysis, for example, cardiovascular disease screening [1], [2]. Many approaches are developed for medical images denoising, e.g., X-ray cardiovascular angiogram images. In [3] a novel smooth and convex surrogate function is first proposed as a replacement of the prior nuclear norm. A novel model called iterative weighted nuclear norm minimization scheme is proposed. In [4] the authors propose a novel iterative weighted sparse representation (IWSR) scheme for X-ray cardiovascular angiogram image denoising. A maximum a posterior (MAP) distribution by the Bayes’ theory is adopted to simultaneously estimate the image and its sparse representation. In [5] the authors have proposed a spatially adaptive image denoising (SAID) method for X-ray angiogram images denoising, which contains two steps: spatially adaptive gradient descent (SAGD) image denoising and dual-domain filter (DDF)
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