RICE: A method for quantitative mammographic image enhancement

•A novel and first of its kind method designed to enhance and quantify focal densities in dense breast. The method identifies normal parenchyma and recursively subtracts it from the breast image, including above and below a potential abnormality such as a dense mass. This enables to unmask tumors embeded in dense breast (examples included). The method quantifies focal densities, thus enabling to measure the extent of assymetry among bilateral mammograms. This is particularly useful when applied to a time series mammographic images, where the method can potentially predict (a case study included) the laterality of cancer.•The first content based mammographic image enhancement method that uses measurable parenchymal contents of breast, unlike existing methods that relies on the appearance of a mamogram and enhances an image by changing the dynamic range, or performing histogram equalisation, low-pass filtering, regional contrast stretching, gamma correction etc.•The method works with regular mammograms as well as other mammographic modalities such as Volpara images, SAR, and synthetic mammograms generated from DBT stacks. The method has a potential to extend to images beyond mammography (example included).