Computational advances applied to medical image processing: an update

摘要

Medical imaging provides a high-fidelity, noninvasive, or minimally invasive means for effective diagnostic and routine checks, and has become an established tool in both clinical and research settings. The interpretation of medical images commonly requires analysis by an experienced individual with the necessary skills. This dependence on an individual's evaluation in part limits the broader scope and widespread use of medical images that would be possible if performed automatically. The analysis of medical images by an individual may also influence reliability, with different users attaining alternative conclusions from the data set. It is thus beneficial to support the experienced user with robust and fast processing of the medical images for further analysis that relies as little as possible on user interaction. In the existing body of literature, a variety of methods have been proposed for medical image filtering and enhancement, which have been largely used in the context of improving image quality for both human visual perception and feature detection and object segmentation via a numerical algorithm. In this study, an analysis of some popular methodologies for image processing is presented. From the comparison of results, a robust and automatic pipeline procedure for medical image processing is put forward, and results for different imaging-acquisition techniques are given.