Segmentation of medical ultrasonic image using hybrid neural network.
Space Med Med Eng (Beijing)
; 14(2): 84-7, 2001 Apr.
Article
en En
| MEDLINE
| ID: mdl-11806427
ABSTRACT
Objective. To solve one of the most difficult problems in multi-dimensional reconstruction of medical ultrasonic images image segmentation. Method. A new segmental method based on hybrid neural network was presented in this paper. The hybrid neural network comprised two phases. The first phase was Kohonens self-organization neural network, which was used to segment and label the image coarsely. The feature vectors of those pixels within a specified distance from the cluster centers were employed to train the second phase--a three-layer perception network using back-propagation (BP) technique. Then the trained BP network was used to label every pixel of the image. In the end, a post-processing stage was used to remove the small isolated points and smooth out the contours of the segmented image. Result. The segmented image had smooth continuous edges, few noises or speckles, and the contour of ventricle was clear and accurate. Conclusion. Our method could segment the ultrasonic images accurately and effectively, and had a lot of advantages compared to traditional methods. The unsupervised segmentation problems could be solved using supervised methods.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Ecocardiografía
/
Redes Neurales de la Computación
Idioma:
En
Revista:
Space Med Med Eng (Beijing)
Asunto de la revista:
ENGENHARIA BIOMEDICA
/
MEDICINA AEROESPACIAL
Año:
2001
Tipo del documento:
Article
País de afiliación:
China