Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels.
Phys Med Biol
; 63(17): 17LT01, 2018 08 29.
Article
en En
| MEDLINE
| ID: mdl-30088812
ABSTRACT
This letter proposes to extract contrast-filled vessels from overlapped noisy complex backgrounds in an x-ray coronary angiogram image sequence using low-rank and sparse decomposition. A refined vessel segmentation is finally achieved by implementing a radon-like feature filtering plus local-to-global adaptive thresholding to tackle the spatially varying noisy residuals in the extracted vessels. Based on real and synthetic XCA data, the experiment results demonstrate the superiority of the proposed method over the state-of-the-art methods.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Vasos Sanguíneos
/
Algoritmos
/
Procesamiento de Imagen Asistido por Computador
/
Angiografía Coronaria
Límite:
Humans
Idioma:
En
Revista:
Phys Med Biol
Año:
2018
Tipo del documento:
Article