In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy cmeans clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assessment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image noise.
Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models
L'Abbate S;
2011-01-01
Abstract
In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy cmeans clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assessment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image noise.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
