In general, in the several modalities of imaging systems, researchers are very much interested in reducing noise of images (that depends on the modality), especially keeping structural details and resolution. Still, several of these modalities as Breast Tomosynthesis and Computed Tomography (CT) use the dangerous ionizing radiation for imaging. Thus, researchers in this area follow the ALARA principle (As Low As Reasonably Achievable), in order to use the lowest possible dose of radiation. However, reducing the dose in these systems implies increased noise in the acquired image. Based on these reasons, this work plan aims at investigating a new Markov Random Fields (MRF) model regarding, especially, the state of the art approach, which uses the redundant information contained in different patches (non local) of an image. This new model will be named Non Local MRF (NLMRF). As main advantages of this new model, we highlight: 1) the use of radiometric information in the selection of MRF cliques by considering the weighting of the energies from the model according to the similarity between the patches, 2) the generation of an adaptive algorithm to the noise type by considering a Bayesian Framework for obtaining the similarity measures between patches more suitable to each noise type and 3) to the local noise level provided by a parameter defined in terms of the Fisher Information. Thus, it is expected that, by using the NLMRF model to image denoising, especially, of CT, Breast Tomosynthesis and Ultrasound, we may achieve a better balance between noise reduction and details preservation such as edges and other structures, when compared to existing methods, besides the possibility of reducing the radiation dose in systems which use ionizing radiation.
News published in Agência FAPESP Newsletter about the scholarship: