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Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The centerline constrained minimal path propagation algorithm for vascular extraction can effectively extract the vessel centerlines and complete vessel structures. The algorithm includes two minimum path based propagations. In the first propagation, it used a novel back-tracing accumulation strategy with convexity symmetry energy function to extract vessel centerlines. With the obtained centerline information used as the constraint, used a second propagation to extract vessel structures. For the proposed approach, only one start point was needed to be set, and no input of start and end points were required for the whole centerline and vessel extraction. It extracted the vessel centerlines and the vessel structure of four 3-D angiography data in the experiment well. [ABSTRACT FROM AUTHOR]

This study proposed a vessel segmentation method based on Gabor features. According to the eigenvector of Hessian matrix of each pixel in the image, the vessel direction of each point was obtained to set the direction angle of Gabor filter, and the Gabor features of different vessel width at each point were extracted to establish the 6D vectors of each point. By reducing the dimension of the 6D vector, the 2D vector of each point was obtained and fused with the original image G channel. U-Net neural network was used to classify the fused image to segment vessels. The experimental results of this method in DRIVE dataset showed that this method had a good effect on the detection of small vessels and vessels at the intersection.