In this paper we present a novel approach for mass contour detection for 3D computer-aided detection (CAD) in
digital breast tomosynthesis (DBT) data-sets. A hybrid active contour model, working directly on the projected
views, is proposed. The responses of a wavelet filter applied on the projections are thresholded and combined
to obtain markers for mass candidates. The contours of markers are extracted and serve as initialization for
the active contour model, which is then used to extract mass contours in DBT projection images. A hybrid
model is presented, taking into account several image-based external forces and implemented using a level-set
formulation. A feature vector is computed from the detected contour, which may serve as input to a dedicated
classifier. The segmentation method is applied to simulated images and to clinical cases. Image segmentation
results are presented and compared to two standard active contour models. Evaluation of the performance on
clinical data is obtained by comparison to manual segmentation by an expert. Performance on simulated images
and visual performance assessment provide further illustration of the performance of the presented approach.
To accurately detect radiological signs of cancer, mammography requires the best possible image quality for a target patient dose. The application of automatic optimization of parameters (AOP) to digital systems has been improved recently. The metric used to derive this AOP was based on the expected CNR of calcium material in a uniform background. In this work, we use a new metric, based on the detection performance of an a-contrario observer on lesions in simulated images. Breast images at various thicknesses and glandularity levels were simulated with flat and textured backgrounds. Various exposure spectra (Mo/Mo, Mo/Rh and Rh/Rh anode/filter materials, kVp ranging from 25 to 33 kV) were considered. The tube output has been normalized in order to obtain comparable AGD values for each image of a given breast over the various acquisition techniques. Images were scored with the a-contrario observer, the performance criterion being the minimal lesion size needed to reach a given detection threshold. The optimal spectra are found similar to those delivered by the AOP in both flat and textured backgrounds. The choice of the anode/filter combination appears to be more critical than kVp adjustments in particular for the thicker breasts. Our approach also yields an estimate of the detection variability due to texture signal. We found that the anatomical structure variability cannot be overcome by beam quality optimization of the current system in presence of complex background, which confirms the potential benefit of any imaging technology reducing the variability of detection due to texture.
Burgess showed that lesion detectability does have a non-trivial behavior with textured mammographic backgrounds: the threshold detectability occurs when the log contrast is linearly related to the log size with positive slope. Grosjean et al. proposed the a-contrario detector as an acceptable observer for detection on such backgrounds. In this study, we quantitatively simulated projected breast images containing lesions with a variety of sizes and thicknesses, for a 55 mm thick, 50/50 glandular breast and with different textured background types generated by the power-law filtered noise model proposed by Burgess. The acquisition parameters used in the simulation correspond to the optimal techniques provided by a digital mammography system for that specific breast. Images have been automatically scored by the a-contrario detector in order to find the minimum thickness of the lesion needed to reach the detection threshold.
Taking into account the Fourier spectrum properties of the breast texture and using the a-contrario observer as a new metric for the detection task, we found the same detection slopes as described by Burgess. With our quantitative simulation, which includes a realistic image chain of a digital mammography system, and with the implementation of a novel detection process, we found that for the considered lesion sizes, lesions are easier to detect on textures with a high value of power-law exponent.