A Mutual Information-Based Image Quality Metric for Medical Imaging Systems

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 chapter, we present a method for quantifying overall image quality of digital imaging systems using mutual information (MI) metric. The MI which is a concept from information theory is used as a measure to express the amount of information that an output image contains about an input object. The MI value is considered that it can be used to express combined physical properties of image noise, resolution and contrast of an imaging system. The higher the MI value, the better the image quality. The advantages of using the MI metric are: (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image contrast, noise and resolution.

[1]  F R Verdun,et al.  A comparison of the performance of digital mammography systems. , 2007, Medical physics.

[2]  B. Schueler,et al.  Performance evaluation of a computed radiography imaging device using a typical "front side" and novel "dual side" readout storage phosphors. , 2006, Medical physics.

[3]  Swatee Singh,et al.  Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance. , 2007, Medical physics.

[4]  E. Samei,et al.  Experimental comparison of noise and resolution for 2k and 4k storage phosphor radiography systems. , 1999, Medical physics.

[5]  Martin Spahn,et al.  Flat detectors and their clinical applications , 2005, European Radiology.

[6]  N J Hangiandreou,et al.  Effects of x-ray spectra on the DQE of a computed radiography system. , 2001, Medical physics.

[7]  Du-Yih Tsai,et al.  Physical characterization of digital radiological images by use of transmitted information metric , 2008, SPIE Medical Imaging.

[8]  Abraham Kandel,et al.  Information-theoretic algorithm for feature selection , 2001, Pattern Recognit. Lett..

[9]  F. Attneave Applications of information theory to psychology: A summary of basic concepts, methods, and results. , 1961 .

[10]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[11]  E. Samei,et al.  A method for measuring the presampled MTF of digital radiographic systems using an edge test device. , 1998, Medical physics.

[12]  Ehsan Samei,et al.  An experimental comparison of detector performance for computed radiography systems. , 2002, Medical physics.

[13]  Du-Yih Tsai,et al.  Information Entropy Measure for Evaluation of Image Quality , 2008, Journal of Digital Imaging.

[14]  Ehsan Samei,et al.  Determination of the detective quantum efficiency of a digital x-ray detector: comparison of three evaluations using a common image data set. , 2004, Medical physics.

[15]  Ehsan Samei,et al.  A method for modifying the image quality parameters of digital radiographic images. , 2003, Medical physics.

[16]  Du-Yih Tsai,et al.  Mutual information-based evaluation of image quality with its preliminary application to assessment of medical imaging systems , 2009, J. Electronic Imaging.