Evaluation of outliers in acquired brain MR images

3rd International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Moldovanu, S.^1,2 ; Pung, M.^1,3 ; Moraru, L.^1

Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunrea de Jos University of Galai, 47 Domneasc St., Galai, Romania^1

Dumitru Mooc High School, 15 Milcov St., Galai

800509, Romania^2

Aurel Vlaicu High School, 1 Decembrie 1918 St., Galai

800511, Romania^3

关键词: Distance functions;    Intensity inhomogeneity;    Magnetic resonance images (MRI);    Probability theory;    Processed images;    Quantitative image analysis;    Similarity scores;    Spatial-histograms;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/574/1/012121/pdf
DOI  :  10.1088/1742-6596/574/1/012121

来源: IOP

【 摘 要 】

Pre-processing is an important stage in the analysis of magnetic resonance images (MRI), because the effect of specific image artefacts, such as intensity inhomogeneity, noise and low contrast can adversely affect the quantitative image analysis. The image histogram is a useful tool in the analysis of MR images given that it allows a close relationship with important image features such as contrast and noise. The noise and variable contrast are elements that locally modify the quality of images. The key issue of this study derives from the fact that the spatial histogram can contain outliers indicating corrupted image information through the disorder of the bins. These aberrant errors should be excluded from the studied data sets. Here, the outliers are evaluated by using rigorous methods based on the probability theory and Chauvenet (CC), Grubbs (GC) and Peirce's (PC) criteria. In order to check the quality of the MR images, the Minkowsky (MD), Euclidean (ED) and cosine (CD) distance functions were used. They act as similarity scores between the histogram of the acquired MRI and the processed image. This analysis is necessary because, sometimes, the distance function exceeds the co-domain because of the outliers. In this paper, 32 MRIs are tested and the outliers are removed so that the distance functions generate uncorrupted and real values.