Three-dimensional reconstruction of microcalcification clusters from two mammographic views

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). The authors 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.

[1]  R Novak Transformation of the female breast during compression at mammography with special reference to the importance for localization of a lesion. , 1988, Acta radiologica. Supplementum.

[2]  M. Tasto X-Ray Image Processing , 1977 .

[3]  J. Wolfe,et al.  Xeroradiography of the breast. , 1983, Geriatrics.

[4]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[5]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[6]  M. Brady,et al.  Detecting film-screen artifacts in mammography using a model-based approach , 1999, IEEE Transactions on Medical Imaging.

[7]  M. Giger,et al.  Malignant and benign clustered microcalcifications: automated feature analysis and classification. , 1996, Radiology.

[8]  Rainer Stotzka,et al.  Volume Reconstruction of Clustered Micro-Calcifications in Mammograms , 1998, Digital Mammography / IWDM.

[9]  D. Sanders Diagnosis and Differential Diagnosis of Breast Calcifications , 1988 .

[10]  Michael Brady,et al.  MRI-Mammography 2D/3D Data Fusion for Breast Pathology Assessment , 2000, MICCAI.

[11]  Daniel B. Kopans,et al.  Digital Breast Tomosynthesis: Potentially a New Method for Breast Cancer Screening , 1998, Digital Mammography / IWDM.

[12]  Jean Ponce,et al.  On reconstructing Curved Object Boundaries from Sparse Sets of X-Ray Images , 1995, CVRMed.

[13]  Michael Brady,et al.  Mammographic Image Analysis , 1999, Computational Imaging and Vision.

[14]  N Karssemeijer,et al.  Automated classification of clustered microcalcifications into malignant and benign types. , 2000, Medical physics.

[15]  Jyrki Lötjönen,et al.  Reconstruction of 3-D geometry using 2-D profiles and a geometric prior model , 1999, IEEE Transactions on Medical Imaging.

[16]  Michael Brady,et al.  Correspondence between different view breast X-rays using a simulation of breast deformation , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).