An information-theoretic quality assessment approach for X-ray images

In this work, Shannon's formulation of the information capacity of a communication channel is considered as a framework for the quantification of image quality. We describe a method to apply Shannon's noisy channel coding theorem on radiographic images, which exhibit signal-dependent noise. Results of a number of experiments on real X-ray images are presented, showing the correlation of the proposed method with classical image quality metrics from Linear Systems Theory.

[1]  Peter H. N. de With,et al.  Toward a full-reference information-theoretic quality assessment method for x-ray images , 2008, SPIE Medical Imaging.

[2]  R. F. Wagner,et al.  Application of information theory to the assessment of computed tomography. , 1979, Medical physics.

[3]  Carl L. Fales,et al.  Visual Communication: An Information Theory Approach , 2010 .

[4]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  H. Arsenault,et al.  Integral expression for transforming signal-dependent noise into signal-independent noise. , 1981, Optics letters.

[7]  M. Bartlett.  The Square Root Transformation in Analysis of Variance , 1936 .

[8]  Suyash P. Awate,et al.  Unsupervised, information-theoretic, adaptive image filtering for image restoration , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  I. Cunningham.  Applied Linear-Systems Theory , 2000 .

[11]  C P Lawinski,et al.  Threshold contrast detail detectability curves for fluoroscopy and digital acquisition using modern image intensifier systems. , 2004, The British journal of radiology.

[12]  Boris Skoric,et al.  Estimating the Secrecy-Rate of Physical Unclonable Functions with the Context-Tree Weighting Method , 2006, 2006 IEEE International Symposium on Information Theory.

[13]  M Fuderer,et al.  The information content of MR images. , 1988, IEEE transactions on medical imaging.

[14]  Han Kroon.  Overall x-ray system simulation model developed for system design and image quality versus patient dose optimization , 2003, SPIE Medical Imaging.

[15]  Rodney Shaw,et al.  Signal-to-noise optimization of medical imaging systems , 1999 .

[16]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.