A Novel Method for Vessel Segmentation and Automatic Diagnosis of Vascular Stenosis A novel method of vessel segmentation in angiocardiography image processing is studied. The process of vessel segmentation is implemented based on Hessian matrix, in which a new vascular function is employed. Subsequently, the vessel contour is extracted from the handled image by using the dualstage region growing. Extracting vessel skeleton, searching skeleton point and measuring diameter are carried out to realize the automatic diagnosis of vascular stenosis based on vessel segmentation. The experimental results show that the stated method for vessel segmentation manages to accurately extract the main branch contour, and even most small vessels. The automatic diagnosis of vascular stenosis can get relatively accurate results, which may provide auxiliary references and quantify the judgment of vascular stenosis for doctors.

Retinal Vessel Segmentation Based on Multiscale Line Detection and Morphological Transformation Retina is an important part of human eyes, analyses of retinal vessels are preferable to the early prevention, detection and treatment of cardiovascular diseases. In this paper, we propose an effective unsupervised segmentation method. Firstly, the image is preprocessed to reduce interference of external factors; Secondly, the preprocessed image is enhanced to suppress the optic disc and background interference; Finally, based on the method of multiscale line detection, the blood vessels images are obtained. We evaluate our algorithm on the public datasets DRIVE and STARE, the experimental results show that the accuracy is about 95.90% and 96.11%, the sensitivity is 76.70% and 75.86%, the specificity is 95.20% and 95.56% respectively. Compared with other unsupervised methods, the proposed algorithm has a better segmentation effect.