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. 1. Introduction Accurate segmentation and quantification of vascular structures in medical images is a critical task for clinical practices such as computer-aided diagnosis, treatment, surgical planning, and navigation. However, it is highly challenging to extract vascular structures in 2D and 3D medical images. The reasons lie in two aspects. On one hand, some vascular structures involve numerous vascular branches and complex patterns [1]. On the other hand, noise, variations in intensities, and low image contrast pose difficulties in vascular tree extraction [2]. Various extraction techniques have been proposed for vascular tree segmentation, that is, pattern recognition techniques, model-based approaches, mathematical morphology, multiscale filtering approaches, vessel tracking, and matched filtering (see Kirbas and Quek [3] and Lesage et al. [4] for comprehensive reviews). Almost all the vascular extraction techniques take advantage of the characteristics of tubular-like or line-like structure of vessels. Among existing vascular extraction methods, Hessian-based multiscale filtering has received much attention [1, 5–12]. These methods share a common idea that the images are convolved with 2D or 3D Gaussian