Vessel segmentation using multiscale vessel enhancement and a region based level set model. (October 2020) - Yang, Jinzhu; Lou, Chunhui; Fu, Jie; Feng, Chaolu
Abstract: Vessel segmentation has always been a considerable challenge task due to the presence of varying thickness of the vessels and weak contrasts of medical image intensities. In this paper, an effective method is proposed, which consists of four steps. Firstly, the input images are converted into gray ones with predetermined weightings aiming at increasing image contrast if they are colorful. Secondly, the image intensities are expanded from regions of interest to the whole image domain with a mirroring operation to avoid introducing undesired boundaries by image filtering operations existing in the next step. Thirdly, an improved multi-scale enhancement method inspired by the Frangi filtering is proposed to enhance image contrast between blood vessels and other objects in the image. Finally, an improved level set model is proposed to segment blood vessels from the enhance images and the original gray images. The proposed method has been evaluated on two retinal vessel image repositories, namely, DRIVE and STARE. Experimental results and comparison with 13 existing methods show that the proposed method produces similar segmentation accuracy at the same level with representative methods in the literature. Its effectiveness on segmentation of other type vessels is also discussed at the end of this paper. Highlights: Colorful images are converted into gray with predetermined weightings. Image intensities are expanded from ROI to the whole image domain. Image contrast is enhanced by an improved Frangi based filtering method. Multiple spectral level set model is proposed to produce final segmentation.