Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images.
Annu Int Conf IEEE Eng Med Biol Soc
; 2007: 3741-4, 2007.
Article
em En
| MEDLINE
| ID: mdl-18002811
ABSTRACT
Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az 0.802+/-0.083) in the discrimination of hepatic tissue.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Interpretação de Imagem Assistida por Computador
/
Tomografia Computadorizada por Raios X
/
Neoplasias Hepáticas
Tipo de estudo:
Diagnostic_studies
/
Evaluation_studies
Limite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
Grécia