Feasibility study of automating breast phantom scoring using image processing techniques

Abstract

Quality control in mammographic facilities has to be done periodically in order to ensure that the mammographic chain works properly. In particular global image quality is evaluated from a mammographic phantom film. A phantom is an object with the same anatomic shape and radiological response as an average dense-fleshed breast in which are embedded structures that mimic clinically relevant features such as microcalcifications, masses and fibers. This evaluation is done by visual observation of a phantom film and a global score is given depending on the number of objects seen by several observers. This paper presents the main results of a feasibility study of breast phantom scoring using digital image processing. First breast phantom films were digitized. For each category of structures, subimages were extracted from the digitized phantom. A noise reduction method was used as a pre-processing step. A local contrast enhancement was then applied. At last image segmentation was done. Noise reduction and contrast enhancement steps were both based on a direct contrast modification technique. Segmentation step was adapted to each embedded object. Nine digitized phantom films were studied and results show that an evaluation of mammographic facilities could be done using digital image processing.