Classification of breast mass abnormalities using denseness and architectural distortion
Baeg, Soon (Texas Instruments, Inc. (Dallas, Estats Units d'Amèrica))
Kehtarnavaz, Nasser (University of Texas at Dallas. Department of Electrical Engineering)

Data:	2002
Resum:	This paper presents an electronic second opinion system for the classification of mass abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the number of benign breast cancer biopsies. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distortion, are extracted from the marked area. The denseness feature provides a measure of radiographic denseness of the marked area, whereas the architectural distortion feature provides a measure of its irregularity. These features are then fed into a neural network classifier. Receiver operating characteristic (ROC) analysis was conducted to evaluate the system performance. The area under the ROC curve reached 0. 90 for the DDSM database consisting of 404 biopsy proven masses. A sensitivity analysis was also performed to examine the robustness of the introduced texture features to variations in sizes of abnormality markings.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Classification of mass abnormality ; Mammography ; Texture features ; Electronic second opinion ; Denseness and architectural distortion ; Classificació d'anormalitat de massa ; Mamografia ; Característiques de textura ; Segona opinió electrònica ; Deformació arquitectònica ; Clasificación de anormalidad de masa ; Mamografía ; Características de textura ; Segunda opinión electrónica ; Deformación arquitectónica
Publicat a:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 1 n. 1 (2002) p. 1-20, ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v1-n1-baeg-kehtarnavaz
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/31591
DOI: 10.5565/rev/elcvia.59

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