An automatic inspection technology for angiostenosis in contrastographic image.
- Author:
Yao FENG
1
;
Ning LIU
;
Yachong FENG
Author Information
1. Department of Radiology, Fourth Affiliated Hospital of Anhui Medical College, Hefei 230022, China. yaoyao2878551@126.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Angiography;
instrumentation;
methods;
Artifacts;
Blood Vessels;
pathology;
Constriction, Pathologic;
diagnostic imaging;
Contrast Media;
Humans;
Pattern Recognition, Automated;
methods;
Radiographic Image Interpretation, Computer-Assisted;
methods
- From:
Journal of Biomedical Engineering
2013;30(2):380-394
- CountryChina
- Language:Chinese
-
Abstract:
This paper presents an automatic calculation method for angiography image, which enables programs to intellectively acquire several parameters of blood vessels, such as contours, segments, widths, etc. and then intellectively identify the angiostenosis parts. This method is a kind of automatic optic inspection (AOI) technology. Blood vessels usually distribute as curves and have a fastening direction. According to this feature, the approach performs inspection automatically using improved Steger algorithm, which firstly computes the convolution between image and Gaussian function kernel, and then computes second order Taylor expansion at eac pixel. And further the eigenvalues and eigenvectors of Hessian matrix are calculated on each pixel to obtain the direction of lines and local maximum of second derivative at that point. Hysteresis threshold and directional connection operators are then used to generate blood vessel skeleton. Finally we can compute the blood vessel widths for every sub-pixel object points on blood vessel curve. For given digital X-ray images of hearts with blood vessel local straitness, experiments showed that this method had the ability of getting all the data we need and could find the local confined parts in blood vessels. This approach is proved to have a good effort for angiography images, and it has some advantages such as fast speed, high accuracy, good robustness and no need for human interventions. It could also be a promising computer aided diagnosis method.
