Automatic Labelling and BI-RADS Characterisation of Mammogram Densities

Intelligent management of medical data is an important field of research in clinical information and decision support systems. Such systems are finding increasing use in the management of patients known to have, or suspected of having, breast cancer. Different types of breast-tissue patterns convey semantic information which is reported by the radiologist when reading mammograms. In this paper, a novel method is presented for the automatic labelling and characterisation of mammographic densities. The presented method is first concerned with the identification of the prominent structures in each mammogram. Subsequently, 'dense tissue' is labelled in a mammogram data set, and BI-RADS classification is performed based on a 2D pdf that is contracted from a "ground truth" data set as well as a shape analysis framework. The presented method can be used in large-scale epidemiological studies which involve mammographic measurements of tissue-pattern, especially since breast-tissue density has been linked to an increased risk of breast cancer

[1]  Michael Brady,et al.  Filtering hint Images for the Detection of Microcalcifications , 2001, MICCAI.

[2]  Robi Polikar,et al.  Automated segmentation and quantitative characterization of radiodense tissue in digitized mammograms , 2002 .

[3]  N. Boyd,et al.  Analysis of mammographic density and breast cancer risk from digitized mammograms. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[4]  V. Velanovich Fractal analysis of mammographic lesions: a feasibility study quantifying the difference between benign and malignant masses. , 1996, The American journal of the medical sciences.

[5]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[6]  N F Boyd,et al.  Automated analysis of mammographic densities and breast carcinoma risk , 1997, Cancer.

[7]  Michael Brady,et al.  Subjective and computer-based characterisation of mammographic patterns , 2003 .

[8]  N. Boyd,et al.  Mammographic densities and breast cancer risk. , 1998, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[9]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[10]  N. Karssemeijer,et al.  Segmentation of suspicious densities in digital mammograms. , 2001, Medical physics.

[11]  Kenneth Falconer,et al.  Fractal Geometry: Mathematical Foundations and Applications , 1990 .

[12]  R. D'Agostino,et al.  A Suggestion for Using Powerful and Informative Tests of Normality , 1990 .

[13]  K. J. Ray Liu,et al.  Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation , 2001, IEEE Transactions on Medical Imaging.

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