Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

Jie Wu; Quentin Besnehard; Cédric Marchessoux

doi:10.1117/12.873193

14 March 2011 Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

Jie Wu, Quentin Besnehard, Cédric Marchessoux

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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79623F (2011) https://doi.org/10.1117/12.873193
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

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Jie Wu, Quentin Besnehard, and Cédric Marchessoux "Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623F (14 March 2011); https://doi.org/10.1117/12.873193

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KEYWORDS

Tissues

Mammography

Image classification

Fractal analysis

Breast

Statistical analysis

Feature extraction

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