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

The authors propose a new application-specific, post-acquisition quality evaluation method for brain magnetic resonance imaging (MRI) images. The domain of a MRI slice is regarded as universal set. Four feature images; greyscale, 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 different combinations of feature sets. In an ideal MRI slice, the four feature sets are identically equal. Degree of distortion in real MRI slice is quantified by fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and is described by the slice Euler number region property. Total quality score is the weighted sum of the five quality scores. The authors' proposed method addresses 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 and operational complexity of the algorithm. They provide experimental results that demonstrate efficacy of their proposed method on good quality images and on common distortions in MRI images of the brain.

Inspec keywords: biomedical MRI; entropy; computational complexity; image texture; feature extraction; brain

Other keywords: feature images; MRI image distortions; quality attribute; lightness; binary operations; Euler number region; image sharpness; application-specific post-acquisition quality evaluation method; MRI slice; local contrast; operational complexity reduction; texture details; image quality evaluation; image texture; image contrast; local entropy; brain MRI images; computational complexity reduction; greyscale images; set analysis; no-reference quality measure; local standard deviation; quality score; brain magnetic resonance imaging images

Subjects: Biology and medical computing; Medical magnetic resonance imaging and spectroscopy; Computational complexity; Computer vision and image processing techniques; Image recognition; Biomedical magnetic resonance imaging and spectroscopy

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