Computer-aided diagnosis of fatty liver based on ultrasonic images / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 726-729, 2006.
Artigo
em Chinês
| WPRIM (Pacífico Ocidental)
| ID: wpr-320497
Biblioteca responsável:
WPRO
ABSTRACT
This study aims to provide a computer-aided method for the diagnosis of fatty liver by B-scan ultrasonic imaging. Fatty liver is referred to the infiltration of triglycerides and other fats of the liver cells, which affected the texture of liver tissue. In this paper, some features including mean intensity ratio, as well as angular second moment, entropy and inverse differential moment of gray level co-occurrence matrix were extracted from B-scan ultrasonic liver images. Feature vectors which indicated two classes of images were created with the four features. Then we used kappa-means clustering algorithm, self-organized feature mapping (SOFM) artificial neural network and back-propagation (BP) artificial neural network to classify these vectors. The accuracy rate of kappa-means clustering algorithm was 100% for normal liver and 63.6% for fatty liver. The results of SOFM neural network showed that the accuracy rate was 84.8% for normal liver and 90.9% for fatty liver. The accuracy rate of neural network was 100% both for normal liver and fatty liver. This technology could detect the characteristics of B-scan images of normal liver and fatty liver more accurately. It could greatly improve the accuracy of the diagnosis of fatty liver.
Texto completo:
Disponível
Base de dados:
WPRIM (Pacífico Ocidental)
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Diagnóstico por Imagem
/
Ultrassonografia
/
Sensibilidade e Especificidade
/
Redes Neurais de Computação
/
Diagnóstico Diferencial
/
Fígado Gorduroso
/
Pulmão
/
Métodos
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2006
Tipo de documento:
Artigo