A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure’s center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention. 1. Introduction Nowadays, the vessel segmentation technique [1–3] is still a bottleneck of medical image processing. As vascular has an extremely complex topology structure, making most of the conventional image segmentation methods difficult to segment vascular structures accurately, so how to fast, accurately and effectively segment vascular structures from medical images becomes an important issue. However, just using simple segmentation method cannot achieve expectant purpose. And effective vascular structure enhancement is also a key point for vascular segmentation. Hessian matrix [4] is often used in enhancement filter on tubular structures. Traditional single scale-based enhancement filter is not well adapted for vascular structures with large-scale changes. Some multiscale filters such as “cole” [5] and steerable filters [6], as well as using the Hessian matrix to determine the local direction of the target [7], however, do not have good and automatic scale selection approach. Region growing algorithm [8, 9] has small calculation complexity and high speed and is widely used in vascular image segmentation. The basic idea of the traditional growth region is to collect pixels that have similar properties together to form a region. Its performance depends largely on the position of seed points and growth conditions. On the other hand, the traditional method needs to select seed points manually in order to ensure the stability. Because the blood vessels have a very wide gray level distribution, making the traditional region growing algorithm and binary segmentation method difficult to accurately segment the vascular area, for the region growing conditions also, depends on range of the image gray