Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics

Abstract

Several clinical image databases are currently available to support scientific research in the medical field. These images are generally used to validate studies based on measuring the sensitivity and specificity of a particular clinical task. In the case of digital mammography, the radiation dose directly influences the quality of the image and consequently the performance of radiologists. Therefore, it is important to conduct studies to find a balance between image quality and radiation dose. Image processing methods are typically employed to optimize this relationship. For the evaluation of these methods, it is crucial to have a mammographic image database with specific characteristics, currently unavailable for scientific use. For example, this image database should contain sets of images from the same patient acquired at different radiation doses with breast lesions in known locations. This is achievable using computational methods for noise and microcalcification insertion into pre-acquired clinical images. In this context, the present work aims to present a cloud-based application for on-demand generation of a clinical mammographic image database with different radiation doses and breast lesions. From a set of pre-acquired clinical digital mammograms, it is possible to create N databases with different characteristics. This technique can also be considered as data augmentation.