Detalles del recurso
Pertenece a:
UCL University College London Eprints
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.
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
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Versión: 1.0
Estado: Final
Palabras clave: Algorithms, 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
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Tipo de Interactividad: Expositivo
Nivel de Interactividad: muy bajo
Audiencia:
Estudiante
- Profesor
- Autor
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Estructura: Atomic
Coste: no
Copyright: sí
Requerimientos técnicos: Browser: Any -
Fecha de contribución: 13-feb-2013
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