Review of medical image quality assessment. (May 2016)

• Record Type: Journal Article
• Authors: Chow, Li Sze; Paramesran, Raveendran
• Is Part Of: Biomedical signal processing and control. Volume 27(2016)
• Page Start: 145
• Page End: 154
• Publication Date: 2016-05
• Subjects: Image Quality Assessment (IQA) -- No reference-IQA (NR-IQA) -- Medical images -- MRI -- CT; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.02.006
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We review the Image Quality Assessment (IQA) for medical images. Majority of IQA was done on Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonic images. No reference - IQA (NR-IQA) is suitable in assessing real world medical images. Designing an ideal medical NR-IQA method is a challenging task. Medical image characteristics and content need to be considered in designing medical NR-IQA. Abstract: Image Quality Assessment (IQA) plays an important role in assessing any new hardware, software, image acquisition techniques, image reconstruction or post-processing algorithms, etc. In the past decade, there have been various IQA methods designed to evaluate natural images. Some were used for the medical images but the use was limited. This paper reviews the recent advancement on IQA for medical images, mainly for Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonic imaging. Thus far, there is no gold standard of IQA for medical images due to various difficulties in designing a suitable IQA for medical images, and there are many different image characteristics and contents across various imaging modalities. No reference-IQA (NR-IQA) is recommended for assessing medical images because there is no perfect reference image in the real world medical imaging. We will discuss and comment on some useful and interesting IQA methods, and then suggest several important factors to be considered in designing a new IQA method for medical images. There is still great potential for research in this area.