Motion perception in medical imaging

A potential drawback of image noise suppression in medical image sequence processing is a possible loss of the apparent motion: making objects appears to move slower or less then they move in reality. For medical imaging application this can be of critical importance, for example myocardium motion in cardiac gated single photon emission computed tomography (SPECT) imaging can differentiate viable muscle from scar tissue. Therefore, in this work we design a set of experiments to measure how human observers perceive apparent motion in the presence of image degradations like noise and blur. In addition we will try to identify relevant image features, based on a visual attention model and a block matching motion estimation method that would allow development of an accurate numerical observer capable of predicting human observer motion perception.

[1]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[2]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[3]  Francesc Massanes,et al.  Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards , 2011, J. Electronic Imaging.

[4]  Yong Wang,et al.  Using Model Trees for Classification , 1998, Machine Learning.

[5]  Kyle J. Myers,et al.  Foundations of Image Science , 2003, J. Electronic Imaging.

[6]  Walter H. Ehrenstein,et al.  Basics of seeing motion , 2003 .

[7]  Jie Yao,et al.  Predicting human performance by a channelized Hotelling observer model , 1992, Optics & Photonics.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Harrison H. Barrett,et al.  Foundations of Image Science , 2003, J. Electronic Imaging.

[10]  Patrick Le Callet,et al.  What we see is most likely to be what matters: Visual attention and applications , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Yongyi Yang,et al.  Numerical observer for cardiac motion assessment , 2010, Electronic Imaging.

[12]  Xin Yan,et al.  Linear Regression Analysis: Theory and Computing , 2009 .

[13]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[14]  Miska M. Hannuksela,et al.  Perceptual quality assessment based on visual attention analysis , 2009, ACM Multimedia.

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[17]  Alan C. Bovik,et al.  The Essential Guide to Image Processing , 2009, J. Electronic Imaging.

[18]  Yongyi Yang,et al.  Learning a Channelized Observer for Image Quality Assessment , 2009, IEEE Transactions on Medical Imaging.

[19]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

[20]  A. Murat Tekalp,et al.  Digital Video Processing , 1995 .

[21]  Liming Zhang,et al.  New strategy for image and video quality assessment , 2010, J. Electronic Imaging.