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References [1] Chandler, D. M. (2013). Seven challenges in image quality assessment: Past, present, and future research. SRN Signal Processing, 2013, art. ID 905685. [2] Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J.,Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.-C. J. (2015). Image database TID2013: Peculiarities results and perspectives. Signal Processing: Image Communication, 30, 57-77. [3] Anbarjafari, G. (2015). An objective no-reference measure of illumination assessment. Measurement Science Review, 15(6), 319

R eferences [1] BOSC, E.—PEPION, R.—Le CALLET, P.—KOPPEL, M.—NDJIKI-NYA, P.—PRESSIGOUT, M.—MORIN, L. : Towards a New Quality Metric for 3-D Synthesized View Assessment, IEEE Journal on Selected Topics in Signal Processing 05 No. 07 (2011), 1332–1343. [2] CONZE, P. H.—ROBERT, P.—MORIN, L. : Objective View Synthesis Quality Assessment, Proc. SPIE 8288, Stereoscopic Displays and Applications XXIII (2012). [3] BATTISTI, F.—BOSC, E.—CARLI, M.—Le CALLET, P.—PERUGIA, S. : Objective Image Quality Assessment of 3D Synthesized Views, Elsevier Signal Processing: Image

. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE signal processing magazine , 26 (1), 98–117. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing , 13 (4), 600–612. Wang, Z., & Li, Q. (2010). Information content weighting for perceptual image quality assessment. IEEE Transactions on image processing , 20 (5), 1185–1198.


The purpose of this work was to develop and validate a Monte Carlo model for a Dual Source Computed Tomography (DSCT) scanner based on the Monte Carlo N-particle radiation transport computer code (MCNP5). The geometry of the Siemens Somatom Definition CT scanner was modeled, taking into consideration the x-ray spectrum, bowtie filter, collimator, and detector system. The accuracy of the simulation from the dosimetry point of view was tested by calculating the Computed Tomography Dose Index (CTDI) values. Furthermore, typical quality assurance phantoms were modeled in order to assess the imaging aspects of the simulation. Simulated projection data were processed, using the MATLAB software, in order to reconstruct slices, using a Filtered Back Projection algorithm. CTDI, image noise, CT-number linearity, spatial and low contrast resolution were calculated using the simulated test phantoms. The results were compared using several published values including IMPACT, NIST and actual measurements. Bowtie filter shapes are in agreement with those theoretically expected. Results show that low contrast and spatial resolution are comparable with expected ones, taking into consideration the relatively limited number of events used for the simulation. The differences between simulated and nominal CT-number values were small. The present attempt to simulate a DSCT scanner could provide a powerful tool for dose assessment and support the training of clinical scientists in the imaging performance characteristics of Computed Tomography scanners.

). Quality metrics for practical face recognition. In 21st International Conference on Pattern Recognition (ICPR 2012), 11-15 November 2012. IEEE, 3103-3107. [7] Lin, W., Kuo, C.-C. J. (2011). Perceptual visual quality metrics: A survey. Journal of Visual Communication and Image Representation, 22(4), 297-312. [8] Wang, Z., Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9(3), 81-84. [9] Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE

,O., Kittler, J., and Poh, N., :Quality controlled multi-modal fusion of biometric experts., In Progress in Pattern Recognition, Image Analysis and Applications, pages 881-890. Springer, 2007. [6] Kalka,N. D., Dorairaj, V.,Shah,Y. N., Schmid, N. A. and Cukic B.,: Image quality assessment for iris biometric., In Proceedings of the 24th Annual Meeting of the Gesellscha it Klassikation, pages 445-452. Springer, 2002. [7] Sandre, S-L and Stevens, M. and Mappes, J.,: The effect of predator appetite, prey warning coloration and luminance on predator foraging decisions, Behaviour

References Aydin, T., Mantiuk, R., Myszkowski, K. and Seidel, H. (2008). Dynamic range independent image quality assessment, ACM Transactions on Graphics 27 (3): 1–10. Banterle, F., Artusi, A., Debattista, K. and Chalmers, A. (2011). Advanced High Dynamic Range Imaging: Theory and Practice , AK Peters (CRC Press), Natick, MA. Banterle, F., Artusi, A., Sikudova, E., Edward, T., Bashford-Rogers, W., Ledda, P., Bloj, M. and Chalmers, A. (2012). Dynamic range compression by differential zone mapping based on psychophysical experiments, ACM Symposium on Applied

International Conference on Imaging Systems and Techniques (IST) , 16-17 July 2012. IEEE, 327-331. [28] Kwan, R.K., Evans, A.C., Pike, G.B. (1999). MRI simulation based evaluation of image processing and classification methods. IEEE Transaction on Medical Imaging , 18 (11), 1085-1097. [29] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncell, E.P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transaction on Image Processing , 13 (4), 600-612. [30] Gonzalez, R.C., Woods, R.E. (2002). Digital Image Processing (second edition) . Prentice

. Kraft, eds.), ACM, 2010, p. 66. [13] WANG, Z.-BOVIK, A. C.-SHEIKH, H. R.-SIMONCELLI, E. P. : Image Quality Assessment: from Error Visibility to Struc- tural Similarity, IEEE Transactions on Image Processing 13 No. 4 (2004), 600-612. [14] XIAO, F. : DCT-Based Video Quality Evaluation - final project for EE392J 2000. [15] COMANICIU, D.-RAMESH, V.-MEER, P. : Kernel-Based Object Tracking, IEEE Transactions On Pattern Analysis and Machine Intelligence (PAMI) 25 No. 5 (2003), 564-575. [16] GOLDSTEIN, B. E. : Cognitive Psychology: Connecting Mind, Research and Everyday

, M. and Pinoli, J.C. (1995). Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model, Signal Processing 41 (2): 225-237. Kristan, M., Pers, J., Perse, M. and Kovacic, S. (2006). A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform, Pattern Recognition Letters 27 (13): 1431-1439. Krotkov, E. (1987). Focusing, International Journal of Computer Vision 1 (3): 223-237. Larson, E.C. and Chandler, D.M. (2010). Most apparent distortion: Full-reference image quality assessment and the