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Characterization of Breast Tissues using Texture Analysis: Normal Breast Tissues and Hamartoma - Tapa blanda

Gar-elnabi, Mohamed; Alport, Michael; Rae, William

 
9783845435664: Characterization of Breast Tissues using Texture Analysis: Normal Breast Tissues and Hamartoma
Tapa blanda
ISBN 10: 3845435666 ISBN 13: 9783845435664
Editorial: LAP LAMBERT Academic Publishing, 2011

Sinopsis

The identification of normal breast tissues in mammograms is an important step in identifying abnormal tissues, or masses. The major focus of this study is to use image-processing techniques to characterize the normal breast tissues. Clustering techniques have been developed to classify the breast tissue into one of 5 different tissue types including fat, glandular, connective, dense tissues and pectoral muscle. The classification was achieved by using a novel technique (dSGLD) involving a subset of the elements (the values on the diagonals) in the Spatial Grey Level Dependence (SGLD) matrix which was calculated using texture features. The ultimate goal of this study is therefore to design and implement a computerized characterization method that mimics the radiologist’s characterization of tissue composition in digitized mammograms. The classification results using dSGLD and SGLD features achieved an acceptable accuracy.

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Reseña del editor

The identification of normal breast tissues in mammograms is an important step in identifying abnormal tissues, or masses. The major focus of this study is to use image-processing techniques to characterize the normal breast tissues. Clustering techniques have been developed to classify the breast tissue into one of 5 different tissue types including fat, glandular, connective, dense tissues and pectoral muscle. The classification was achieved by using a novel technique (dSGLD) involving a subset of the elements (the values on the diagonals) in the Spatial Grey Level Dependence (SGLD) matrix which was calculated using texture features. The ultimate goal of this study is therefore to design and implement a computerized characterization method that mimics the radiologist's characterization of tissue composition in digitized mammograms. The classification results using dSGLD and SGLD features achieved an acceptable accuracy.

Biografía del autor

The Author born in 1965-Sudan, joined the University studies at Sudan University of Science and Technology, College of Medical Radiologic Sience and graduated in 1998 with B. Sc. degree in Radiation Therapy, and awarded M.Sc. in Radiation Therapy in 2000 SUST and Awarded Ph.D. in Medical Physics 2007-Natal University-South Africa.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2011
Idioma
Inglés
ISBN 10 
3845435666
ISBN 13 
9783845435664
Encuadernación
Tapa blanda
Número de páginas
180
Contacto del fabricante
no disponible
Persona responsable
no disponible