Author(s): Varvara Nik, Hongmei Zhu, Paul Babyn
Change detection algorithms aim to identify regions of changes in multiple images of the same anatomical location taken at different times. The ability to identify the changes efficiently and automatically is a powerful tool in medical diagnosis and treatment. Although many have investigated ways of automatic change detection algorithms, challenges still remain. The key of detecting changes in medical images is to detect disease-related changes while rejecting unimportant ones induced by noise, mis-alignment, and other common acquisition-related artifacts (such as intensity, inhomogeneity). In this paper, we propose a new approach for detecting local changes based on adaptive dictionary learning techniques. The proposed AEDL, Adaptive EigenBlock Dictionary Learning, algorithm captures local spatial difference between the reference and test images via detecting the significant changes between the test and the reference image linearly modeled by a local dictionary trained from the reference and the test images and reconstructed by local sparse minimization processes. The AEDL algorithm is designed to ignore insignificant changes due to mis-alignment (such as spatial shift, rotation), field inhomogeneity, and noise. To reduce the size of local dictionaries in the algorithm and to identify the linear relationship in the data, the principle component analysis is employed, which helps to speed up the computation for practical applications. Performance of our algorithm is validated using synthetic and real images.