Automatic breast tissue density estimation scheme in digital mammography images

Renan C. Menechelli; Ana Luisa V. Pacheco; Homero Schiabel

doi:10.1117/12.2253186

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10 March 2017 Automatic breast tissue density estimation scheme in digital mammography images

Renan C. Menechelli, Ana Luisa V. Pacheco, Homero Schiabel

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Proceedings Volume 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment; 101361J (2017) https://doi.org/10.1117/12.2253186
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes – taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

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Renan C. Menechelli, Ana Luisa V. Pacheco, and Homero Schiabel "Automatic breast tissue density estimation scheme in digital mammography images", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101361J (10 March 2017); https://doi.org/10.1117/12.2253186

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