Abstract
AIM: We carry out a study on image quality methods, organize them in a logical approach, provide the mathematical framework, and finally discuss their performance. Objective image quality measures are frequently used in image processing. This review is not dedicated to subjective tests as they are very difficult and time consuming processes. MATERIALS AND METHODS: The quality studies are performed during the pre-processing step through the assessment of the de-noising efficacy, during the processing step as segmentation operation and as methods that evaluated its performance or in pattern-recognition. Extensive studies have taken hybrid metrics into account such as structural similarity index (SSIM), mean SSIM, feature similarity, the quality index based on local variance, and objective metrics such as signal-to-noise ratio, peak signal-to-noise ratio, mean square error, mean absolute error, contrast to noise ratio, root mean square error, Bhattacharyya coefficient or mutual information. RESULTS: These methods are compared in the context of brain MR images based on the reported performances. The most frequently used objective metric in the evaluation of the quality of processed MR image is SNR because it is slightly tissue-depended. From the hybrid metrics, the most used is SSIM. CONCLUSION: This paper summarized the objective and hybrids metrics that are Human Vision System - based characteristics. Also, it discusses on the notion of image quality assessment. The problems faced by various metrics are highlighted and the advantage of utilized a certain metric or a tandem of certain metrics are emphasized.
Highlights
Magnetic Resonance Imaging (MRI) gives sound and detailed information about internal tissue structures and has a major influence in brain image analysis and diagnosis field
Extensive studies have taken hybrid metrics into account such as structural similarity index (SSIM), mean SSIM, feature similarity, the quality index based on local variance, and objective metrics such as signal-to-noise ratio, peak signal-to-noise ratio, mean square error, mean absolute error, contrast to noise ratio, root mean square error, Bhattacharyya coefficient or mutual information
In the MR image acquisition terms, the data consist of both discrete Fourier samples, usually referred to as k-space samples and magnitude MR imaging, when the image phase is disregarded and only the magnitude is of interest
Summary
Magnetic Resonance Imaging (MRI) gives sound and detailed information about internal tissue structures and has a major influence in brain image analysis and diagnosis field. MR images suffer from various artefacts as image inhomogeneity, noise (usually, Rician type), patient motion or extra cranial tissues that reduce the overall accuracy. All of these can lead to misinterpretation of brain MR data or even can hinder the practicians’ access to the useful information. Brain disorder diagnosis and brain disorders classification by using MR images are specific medical image analysis methodologies that require a superior image quality. It is important to predict and to enhance the quality of the image. For these reasons, image quality assessment (IQA) is a challenging task for digital image processing. An extensive comparative analysis is performed to illustrate the merits and demerits of various objective and hybrid image quality assessment techniques
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