Characterization of breast nodules in digital images of ultrasonography, elastography and mammography using intelligent techniques

Abstract

Aiming to deal with the clinical worsening of breast cancer as well as to assist its early detection, many imaging diagnosis procedures have been developed or improved regarding both the image and the equipment. Modules of mammography CADx (Computer aided diagnosis) schemes have been proposed with the aim of providing diagnostic support, improving its accuracy. Among the available screening techniques, mammography by X-ray is dominant in the assessment of breast abnormalities. Ultrasound imaging uses to be the main complementary procedure with this intent. In order to reduce more significantly the number of unnecessary biopsies, elastography is a new technique of breast images acquisition, aiming also to increase the accuracy in mammographic findings interpretation. Thus, as part of a wider research concerning a CADx scheme encompassing the latest techniques for breast image acquisition and interpretation, this proposal intends to advance the detection and subsequent classification of suspicious findings, considering a comparative approach of the imaging by X-ray, ultrasound and elastography. The purpose is to set up a model for processing and classification of detected lesions, to be optimized according to the best features present in digital images obtained by those three types of technology. The model should take the use of processing techniques and investigate artificial intelligence algorithms to select the most efficient in the characterization of lesions according to the images set in each case.