Abstract
Information on physical image quality of medical images is important for imaging system assessment in order to promote and stimulate the development of state-of-the-art imaging systems. In this paper, we present a method for evaluating physical performance of medical imaging systems. In this method, mutual information (MI) which is a concept from information theory was used to measure combined properties of image noise and resolution of an imaging system. In our study, the MI was used as a measure to express the amount of information that an output image contains about an input object. The more the MI value provides, the better the image quality is. To validate the proposed method, computer simulations were performed to investigate the effects of noise and resolution degradation on the MI, followed by measuring and comparing the performance of two imaging systems. Our simulation and experimental results confirmed that the combined effect of deteriorated blur and noise on the images can be measured and analyzed using the MI metric. The results demonstrate the potential usefulness of the proposed method for evaluating physical quality of medical imaging systems.
Highlights
An important criterion for accepting any type of medical imaging system is the quality of the images produced by the imaging systems
The results indicate that mutual information (MI) value increases with the increase of signal-to-noise ratio (SNR)
The results indicate that MI value decreases when filter size of the blurring function increases
Summary
An important criterion for accepting any type of medical imaging system is the quality of the images produced by the imaging systems. The most fundamental qualityrelated factors in medical imaging systems are contrast, spatial resolution and noise. In this study we present an information-entropy-based approach for evaluating overall image quality (including image noise and spatial resolution in this study) in medical imaging systems. Differing from the MTF and NPS measures, this information-entropy-based metric is described in the spatial domain. The primary motivation behind this study was to use the MI to express the amount of information that an output image contains about an input object (subject). The focus of this paper is to investigate and characterize the combined effect of noise and blur on the images obtained from medical imaging systems using the proposed metric. The advantages of our proposed method are: 1) simplicity of computation, 2) simplicity of experimentation, and 3) combined assessment of image noise and resolution
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