No-reference quality measure in brain MRI images using binary operations, texture and set analysis

Michael Osadebey, Marius Pedersen, Douglas Arnold, Katrina Wendel-Mitoraj
2017 IET Image Processing  
We propose a new application-specific post-acquisition quality evaluation method for brain MRI images. The domain of a MRI slice is regarded as the universal set. Four feature images; grayscale, local entropy, local contrast and local standard deviation are extracted from the slice and transformed into the binary domain. Each feature image is regarded as a set enclosed by the universal set. Four qualities attribute; lightness, contrast, sharpness and texture details are described by four
more » ... nt combinations of the feature sets. In an ideal MRI slice the four feature sets are identically equal. The degree of distortion in real MRI slice is quantified by the fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and it is described by the slice Euler number region property. The total quality score is the weighted sum of the five quality scores. Our proposed method addresses the current challenges in image quality evaluation. It is simple, easy-to-use and easy-to-understand. Incorporation of binary transformation in the proposed method reduces computational as well as operational complexity of the algorithm. We provide experimental results that demonstrate the efficacy of our proposed method on good quality images and on common distortions in MRI images of the brain.
doi:10.1049/iet-ipr.2016.0560 fatcat:6chqj3trqjenzaea6lnxxeic5i