Current CAD systems always demand better performance, both in terms of the best sensitivity-specificity tradeoff and of the processing time. In order to decrease the false positive rate and to increase the time responsiveness of our CAD system, we present a powerful algorithm that performs an intra-breast segmentation. Starting from a digital mammogram and by using a combination of different techniques, the proposed algorithm produces a binary map showing the locations of the breast with higher probability of containing a suspect mass. Experimental trials performed on 317 mammograms show that our algorithm is able to reduce the intra-breast searching area down to 6% of the entire breast area, while maintaining almost the same negative rate. Typical dimension of images is about 2800×2000 pixels at 13 bits of gray level resolution. The set of images was gathered from FFDM units coming from two different hospitals.
R. Campanini, E. Angelini, E. Iampieri, N. Lanconelli, M. Masotti, M. Roffilli, et al. (2004). A fast algorithm for intra-breast segmentation of digital mammograms for CAD systems. s.l : s.n.
A fast algorithm for intra-breast segmentation of digital mammograms for CAD systems
CAMPANINI, RENATO;LANCONELLI, NICO;MASOTTI, MATTEO;ROFFILLI, MATTEO;
2004
Abstract
Current CAD systems always demand better performance, both in terms of the best sensitivity-specificity tradeoff and of the processing time. In order to decrease the false positive rate and to increase the time responsiveness of our CAD system, we present a powerful algorithm that performs an intra-breast segmentation. Starting from a digital mammogram and by using a combination of different techniques, the proposed algorithm produces a binary map showing the locations of the breast with higher probability of containing a suspect mass. Experimental trials performed on 317 mammograms show that our algorithm is able to reduce the intra-breast searching area down to 6% of the entire breast area, while maintaining almost the same negative rate. Typical dimension of images is about 2800×2000 pixels at 13 bits of gray level resolution. The set of images was gathered from FFDM units coming from two different hospitals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
