Detection improvement in spatially filtered x-ray fluoroscopy image sequences.

The effect of spatial noise-reduction filtering on human observer detection of stationary cylinders mimicking arteries, catheters, and guide wires in x-ray fluoroscopy was investigated in both single image frames and image sequences. Ideal edge-preserving spatial filtering was simulated by filtering of the noise before addition of the target cylinder. This allowed us to separate the effect of edge blurring from those of noise reduction and spatial noise correlation. We used three different center-weighted averagers that reduced pixel noise variance by factors of 0.75, 0.50, and 0.25. As compared with no filtering, the effect of filtering on detection in single images was statistically insignificant. This indicated an adverse effect of spatial noise correlation on detection that countered the effect of noise reduction. By comparison, spatial filtering significantly improved detection in image sequences and yielded potential x-ray dose savings of 26-34%. Comparison of results with two observer models suggested that human observers have an improved detection efficiency in spatially filtered image sequences as compared with white-noise sequences. Pixel noise reduction, a measure commonly used to assess filter performance, overestimated the effect of filtering on detection and was not a good indicator of image quality. We conclude that edge-preserving spatial filtering is more effective in sequences than in single images and that such filtering can be used to improve image quality in noisy image sequences such as x-ray fluoroscopy.

[1]  H H Barrett,et al.  Hotelling trace criterion and its correlation with human-observer performance. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[2]  D L Wilson,et al.  Pulsed fluoroscopy detectability from interspersed adaptive forced-choice measurements. , 1996, Medical physics.

[3]  C W Thomas,et al.  Perceptual comparison of pulsed and continuous fluoroscopy. , 1994, Medical physics.

[4]  David L. Wilson,et al.  Perceived noise versus display noise in temporally filtered image sequences , 1996, J. Electronic Imaging.

[5]  Alexander A. Sawchuk,et al.  Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Guang-Zhong Yang,et al.  Structure adaptive anisotropic image filtering , 1996, Image Vis. Comput..

[7]  M P Eckstein,et al.  Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  Aggelos K. Katsaggelos,et al.  Noise reduction filters for dynamic image sequences: a review , 1995, Proc. IEEE.

[9]  D L Wilson,et al.  Role of phase information and eye pursuit in the detection of moving objects in noise. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  A E Burgess,et al.  Visual signal detectability with two noise components: anomalous masking effects. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  D. H. Kelly Motion and vision. II. Stabilized spatio-temporal threshold surface. , 1979, Journal of the Optical Society of America.

[12]  C E Metz,et al.  Digital image processing: effect on detectability of simulated low-contrast radiographic patterns. , 1984, Radiology.

[13]  D L Wilson,et al.  Effects of motion blurring in x-ray fluoroscopy. , 1998, Medical physics.

[14]  KIM T. BLACKWELL,et al.  PII: S0042-6989(97)00130-2 , 2003 .

[15]  B. Julesz,et al.  Spatial-frequency masking in vision: critical bands and spread of masking. , 1972, Journal of the Optical Society of America.

[16]  David L. Wilson,et al.  X-ray fluoroscopy spatio-temporal filtering with object detection , 1995, IEEE Trans. Medical Imaging.

[17]  Aggelos K. Katsaggelos,et al.  Image sequence filtering in quantum-limited noise with applications to low-dose fluoroscopy , 1993, IEEE Trans. Medical Imaging.

[18]  D L Wilson,et al.  Perception of fluoroscopy last-image hold. , 1994, Medical physics.

[19]  David L. Wilson,et al.  An adaptive reference/test paradigm: Application to pulsed fluoroscopy perception , 1998 .

[20]  H H Barrett,et al.  Effect of random background inhomogeneity on observer detection performance. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[21]  P. Xue,et al.  Detection of moving objects in pulsed-x-ray fluoroscopy. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[22]  A. Burgess Statistically defined backgrounds: performance of a modified nonprewhitening observer model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[24]  Gonzalo R. Arce,et al.  Multistage order statistic filters for image sequence processing , 1991, IEEE Trans. Signal Process..

[25]  Toshikazu Matsui Theoretical analysis of perceptual responses to flashed sinusoidal waves using a multichannel spatiotemporal human vision model , 1998 .

[26]  T. Shope,et al.  Radiation-induced skin injuries from fluoroscopy. , 1996, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  Miguel P. Eckstein,et al.  Effect of additive noise, signal contrast, and feature motion on visual detection in structured noise , 1996, Medical Imaging.

[28]  C W Thomas,et al.  Model for perception of pulsed fluoroscopy image sequences. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[29]  H. Barrett,et al.  Effect of noise correlation on detectability of disk signals in medical imaging. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[30]  Nelson H. C. Yung,et al.  Performance evaluation of a feature-preserving filtering algorithm for removing additive random noise in digital images , 1996 .

[31]  H Roehrig,et al.  Effect of noise on the modulation transfer function of the visual channel. , 1970, Journal of the Optical Society of America.

[32]  Murat Kunt,et al.  Characterization of human visual sensitivity for video imaging applications , 1998, Signal Process..