Computer-Aided Detection Systems for Digital Mammography

Vista sencilla de ítem
dc.contributor.authorPerez Diaz, Marlen
dc.contributor.authorOrozco Morales, Rubén
dc.contributor.authorSuarez Aday, Ernesto Daniel
dc.contributor.authorPirchio, Rosario
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Dpto de Automáticaen_US
dc.contributor.otherMedical Physics Department, Atomic Energy National Commission, Buenos Aires, Argentinaen_US
dc.coverage.spatialSwitzerlanden_US
dc.date.accessioned2022-02-17T16:26:20Z
dc.date.available2022-02-17T16:26:20Z
dc.date.issued2019-10-02
dc.description.abstractMammography is the typical diagnostic test for early detection of breast cancer. The presence of microcalcifications and masses in the images may be an indicator of the disease. The microcalcifications size is very small, so, in many cases, they are not visible from medical images by radiologists. On the other hand, masses can be also undetectable if image contrast is not good enough. The computer-aided detection (CAD) systems are useful tools in facilitating the physician´s diagnosis. The CAD system proposed in this work is aimed at improving image quality based on image processing. On these improved images, it segments the mammary gland and highlights the presence of microcalcifications and masses. The system improves image contrast by means of convolution filters, it also eliminates artifacts by means of morphological opening and closing and Laplacian filtering, and uses entropy-based methods for segmentation of the gland and morphological filtering and histogram readjustment to enhance microcalcifications in the image. Masses are detected using an iterative contrast increase method. The system was tested with an annotated database (DB) MIAS, in oblique lateral views of glandular, glandular-dense and predominantly adipose breasts, which included images of malignant and benign lesions and other breast images without them. The system was evaluated with respect to the DB annotation, for a sample of 115 images. The performance of the system revealed a sensitivity of 93.2%, a specificity of 85.3%, a precision of 90.4% and an accuracy of 92%.en_US
dc.identifier.doi10.1007/978-3-030-30648-9_34en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13438
dc.language.isoen_USen_US
dc.relation.conferenceCLAIB 2019en_US
dc.subjectComputed aided diagnostic systemsen_US
dc.subjectMammographyen_US
dc.subjectMicrocalcifications and massesen_US
dc.titleComputer-Aided Detection Systems for Digital Mammographyen_US
dc.typeProceedingsen_US

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Perez-Diaz2020_Chapter_Computer-AidedDetectionSystems.pdf
Tamaño:
484.96 KB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.33 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Ponencias - Centro de Estudios de Mecánica Computacional y Métodos Numéricos en la Ingeniería (CIMNE).