Line and boundary detection in speckle images

This paper considers the problem of detecting lines in speckle imagery, such as that produced by synthetic aperture radar (SAR) or ultrasound techniques. Using the physical principles that account for the speckle phenomenon, we derive the optimal detector for lines in fully developed speckle, and we compare the optimal detector to several suboptimal detection rules that are more computationally efficient. We show that when the noise is uncorrelated, a very simple suboptimal detection rule is nearly optimal, and that even in colored speckle, a related class of detectors can approach optimal performance. Finally, we also discuss the application of this technique to medical ultrasonic images, where the detection of tissue boundaries is considered as a problem of line detection.

[1]  C. Burckhardt.  Speckle in ultrasound B-mode scans , 1978, IEEE Transactions on Sonics and Ultrasonics.

[2]  Ed Anderson,et al.  LAPACK Users' Guide , 1995 .

[3]  Douglas L. Jones,et al.  An approach to boundary detection in ultrasound imaging , 1993 .

[4]  W T Rhodes,et al.  Invariant pattern recognition using angular signature functions. , 1993, Applied optics.

[5]  Roberto H. Bamberger,et al.  Orientation selective operators for ridge, valley, edge, and line detection in imagery , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  P. M. Shankar,et al.  Ultrasound echo evaluation by K-distribution , 1993 .

[7]  T. Loupas,et al.  An adaptive weighted median filter for speckle suppression in medical ultrasonic images , 1989 .

[8]  Alan C. Bovik,et al.  Boundary detection in speckle images , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Alan C. Bovik,et al.  On detecting edges in speckle imagery , 1988, IEEE Trans. Acoust. Speech Signal Process..

[10]  J.M. Reid,et al.  Use of non-Rayleigh statistics for the identification of tumors in ultrasonic B-scans of the breast , 1993, IEEE Trans. Medical Imaging.

[11]  Douglas L. Jones,et al.  Decision-directed line detection with application to medical ultrasound , 1996, Medical Imaging.

[12]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[13]  L. C. Wood,et al.  Seismic signal processing , 1975, Proceedings of the IEEE.

[14]  K. D. Donohue,et al.  Maximum likelihood estimation of A-scan amplitudes for coherent targets in media of unresolvable scatterers , 1992, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[15]  Douglas L. Jones,et al.  Ultrasound speckle reduction by directional median filtering , 1995, Proceedings., International Conference on Image Processing.

[16]  William T. Rhodes,et al.  Feature Detection And Enhancement By A Rotating Kernel Min-Max Transformation , 1990, Optics & Photonics.

[17]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.

[18]  O DudaRichard,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972 .

[19]  Laurence G. Hassebrook,et al.  Parametric and nonparametric edge detection for speckle degraded images , 1993 .

[20]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.

[21]  R. F. Wagner,et al.  Fundamental correlation lengths of coherent speckle in medical ultrasonic images , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[22]  Patrick Duvaut,et al.  Optimal linear-quadratic systems for detection and estimation , 1988, IEEE Trans. Inf. Theory.

[23]  Patrick J. Flynn,et al.  A Robust System for Lineament Analysis of Aero-magnetic Imagery using Orientation Analysis and Edge Linking , 1994, ICIP.

[24]  W T Rhodes,et al.  Nonlinear image processing by a rotating kernel transformation. , 1990, Optics letters.

[25]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[26]  J.L.C. Sanz,et al.  Image reconstruction from frequency-offset Fourier data , 1984, Proceedings of the IEEE.

[27]  W.D. O'Brien,et al.  Ultrasound data acquisition system design for collecting high quality RF data from beef carcasses in the slaughterhouse environment , 1992, IEEE 1992 Ultrasonics Symposium Proceedings.

[28]  C. R. Moloney,et al.  A comparison of adaptive filters for edge-preserving smoothing of speckle noise , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[29]  M Tur,et al.  When is speckle noise multiplicative? , 1982, Applied optics.

[30]  J. Goodman.  Statistical Properties of Laser Speckle Patterns , 1963 .

[31]  C. Baker.  Optimum Quadratic Detection of a Random Vector in Gaussian Noise , 1966 .

[32]  William T. Rhodes,et al.  Scale- and rotation-invariant pattern recognition by a rotating kernel min-max transformation , 1990, Optics & Photonics.

[33]  Henry Stark,et al.  Probability, Random Processes, and Estimation Theory for Engineers , 1995 .

[34]  Douglas L. Jones,et al.  Edge detection in ultrasound speckle noise , 1994, Proceedings of 1st International Conference on Image Processing.

[35]  Alexander A. Sawchuk,et al.  Adaptive restoration of images with speckle , 1987, IEEE Trans. Acoust. Speech Signal Process..