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Detalles del recurso

Descripción

X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.

Pertenece a

UCL University College London Eprints  

Autor(es)

Mertzanidou, T -  Hipwell, J -  Cardoso, MJ -  Zhang, X -  Tanner, C -  Ourselin, S -  Bick, U -  Huisman, H -  Karssemeijer, N -  Hawkes, D - 

Id.: 55229923

Idioma: eng  - 

Versión: 1.0

Estado: Final

Palabras claveAlgorithms, Breast Neoplasms, Female, Humans, Image Interpretation, Computer -  Assisted, Imaging, Three -  Dimensional, Magnetic Resonance Imaging, Mammography, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique - 

Tipo de recurso: Article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] Med Image Anal , 16 (5) 966 - 975. (2012)

Fecha de contribución: 13-ago-2013

Contacto:

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