Classification of histological images of the endometrium using texture features.
Anal Quant Cytopathol Histpathol
; 35(2): 105-13, 2013 Apr.
Article
en En
| MEDLINE
| ID: mdl-23700719
OBJECTIVE: To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium. STUDY DESIGN: A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features. RESULTS: The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%. CONCLUSION: The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reconocimiento de Normas Patrones Automatizadas
/
Redes Neurales de la Computación
/
Neoplasias Endometriales
/
Citometría de Imagen
/
Endometrio
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Adult
/
Female
/
Humans
Idioma:
En
Revista:
Anal Quant Cytopathol Histpathol
Año:
2013
Tipo del documento:
Article
País de afiliación:
Grecia
Pais de publicación:
Estados Unidos