Classification of benign/malignant microcalcification clusters is a major diagnostic challenge for radiologists. Clinical studies have revealed that the shape of the cluster, and the spatial distribution of individual microcalcifications within it, are important indicators of its malignancy. However, mammographic images of clustered microcalcifications confound their three-dimensional (3-D) distribution with image projection and breast compression. This paper presents a novel model-based method for reconstructing microcalcification clusters in 3-D from two mammographic views (cranio-caudal and medio-lateral oblique--"shoulder to the opposite hip" or lateral-medio). We develop a 3-D breast representation and a parameterised breast compression model which constraints geometrically the possible 3-D positions of a calcification in a two-dimensional image. Corresponding calcifications in the two views are matched using an estimate of the calcification volume. Both the geometric constraint and the matching criterion are utilized in the final reconstruction step to build the 3-D reconstructed clusters. Validation experiments are described using 30 clusters to verify the individual steps of the model, and results consistent with known ground truth are obtained. Some of the approximations in the model and future work are discussed in the concluding section.

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Abstract.The hint representation is a normalised, quantitative version of a mammogram which has substantial quantum noise components because of the way in which it is computed. This paper presents a physics-based approach to de-noising the hint representation of a mammogram. We investigate the major contributions to noise and the steps in the hint generation that amplify noise, such as removal of intensifying screen glare. Estimating the radiographic noise components using parameters derived from physics models, we filter the original mammographic images with an adaptive wiener filter, W . Generating the hint representation from the filtered images yields a de-noised version which has substantially improved signal-to-noise ratio, and which is far better to use for further-processing, such as microcalcification detection. The accuracy of the de-noised hint representation is verified using experimental results on phantom images and mammograms with microcalcifications.

Abstract. Increasing use is being made of contrast-enhanced MagneticResonance Imaging (Gd-DTPA) for breast cancer assessment since it provides 3D functional information via pharmacokinetic interaction between contrast agent and tumour vascularity, and because it is applicable to women of all ages. Contrast-enhanced MRI (CE-MRI) is complimentary to conventional Xray mammography since it is a relatively low-resolution functional counterpart of a comparatively high-resolution 2D structural representation. However, despite the additional information provided by MRI, mammography is still an extremely important diagnostic imaging modality, particularly for several common conditions such as ductal carcinoma in-situ (DCIS) where it has been shown that there is a strong correlation between microcalcification clusters and malignancy [1]. Pathological indicators such as calcifications and fine spiculations are not visible in CE-MRI and therefore there is clinical and diagnostic value to fusing the high-resolution structural information available from mammography with the functional data acquired from MRI imaging. This paper presents a novel data fusion technique whereby medio-lateral (ML) and cranio-caudal (CC) mammograms (2D data) are registered to 3D contrastenhanced MRI volumes. We utilise a combination of pharmacokinetic modelling, projection geometry, wavelet-based landmark detection and thinplate spline non-rigid registration to transform the coordinates of regions of interest (ROIs) from the 2D mammograms to the spatial reference frame of the contrast-enhanced MRI volume.

Core biopsies of an area of microcalcification demonstrated large collections of macrophages containing haemosiderin, with evidence of minimal microcalcification on H&E staining. Algorithms were developed that were capable of differentiating with high accuracy those signs due to calcification, using quantitative measurements such as the apparent volume composition of calcium. Using the linear attenuation coefficients of calcification and assuming an ellipsoid model for the 3-dimensional shape of calcification, we computed the relative calcification volume for each region of interest. The difference in the linear attenuation coefficients of iron and calcification allowed the two to be differentiated on a mammogram based on this measure of relative calcification volume.

Recent figures show that approximately 1 in 11 women in the western world will develop breast cancer during the course of their lives. Early detection greatly improves prognosis and considerable research has been undertaken to this end. Mammographic images are difficult to interpret even by radiologists and this makes their task error prone. One of the earliest non-palpable signs is the appearance of microcalcifications, typically 0.5 mm in diameter, representing small deposits of calcium salts in the breast. A novel approach to detecting microcalcifications in x-ray mammography has been explored. The method is based on the use of the physics-based image representation hint [1] and use of anisotropic diffusion to filter hint images. The diffusion process becomes a method of detecting both noise and microcalcifications in mammograms.

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