Two-Steps Coronary Artery Segmentation Algorithm Based on Improved Level Set Model in Combination with Weighted Shape-Prior Constraints

Due to the complex topological structure of the coronary artery and the uneven distribution of the contrast agent, the angiography images are inevitably blurred and has low contrast, which causes great difficulty in process of segmentation. For this problem, a two-steps segmentation algorithm based...

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Vydáno v:Journal of medical systems Ročník 43; číslo 7; s. 210 - 10
Hlavní autoři: Ge, Shang, Shi, Zhaofei, Peng, Guangming, Zhu, Zhaohuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2019
Springer Nature B.V
Témata:
Algorithms
Angiography
Blood vessels
Constraints
Contrast agents
Coronary artery
Coronary vessels
Coronary Vessels - diagnostic imaging
Coronary Vessels - pathology
Distributed Analytics and Deep Learning in Health Care
Eigenvalues
Feature extraction
Health Informatics
Health Sciences
Hessian matrices
Humans
Image & Signal Processing
Image contrast
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Mathematical analysis
Matrix algebra
Matrix methods
Medicine
Medicine & Public Health
Regularization
Response functions
Statistics for Life Sciences
Tomography, X-Ray Computed - methods
Veins & arteries
Shape-prior constraint
Segmentation
Level set
Coronary artery
Two-steps segmentation
Active contour model
Energy fitting
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí:Due to the complex topological structure of the coronary artery and the uneven distribution of the contrast agent, the angiography images are inevitably blurred and has low contrast, which causes great difficulty in process of segmentation. For this problem, a two-steps segmentation algorithm based on Hessian matrix and level set is proposed in this paper. Firstly, potential blood vessels of coronary images are preliminary extracted via Hessian matrix eigenvalues feature vectors of the geometric features and the response function. Then a novel regularization and area constraint is introduced into the local data energy fitting functional. Finally, the precision of Coronary Artery image is obtained in the evolution of the level set function. Experiments show that our proposed algorithm has better performance to these comparison segmentation algorithms.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1329-y