The 2D analytic signal for envelope detection and feature extraction on ultrasound images

The fundamental property of the analytic signal is the split of identity, meaning the separation of qualitative and quantitative information in form of the local phase and the local amplitude, respectively. Especially the structural representation, independent of brightness and contrast, of the local phase is interesting for numerous image processing tasks. Recently, the extension of the analytic signal from 1D to 2D, covering also intrinsic 2D structures, was proposed. We show the advantages of this improved concept on ultrasound RF and B-mode images. Precisely, we use the 2D analytic signal for the envelope detection of RF data. This leads to advantages for the extraction of the information-bearing signal from the modulated carrier wave. We illustrate this, first, by visual assessment of the images, and second, by performing goodness-of-fit tests to a Nakagami distribution, indicating a clear improvement of statistical properties. The evaluation is performed for multiple window sizes and parameter estimation techniques. Finally, we show that the 2D analytic signal allows for an improved estimation of local features on B-mode images.

[1]  Jørgen Arendt Jensen,et al.  A new calculation procedure for spatial impulse responses in ultrasound , 1999 .

[2]  J. Greenleaf,et al.  Ultrasound echo envelope analysis using a homodyned K distribution signal model. , 1994, Ultrasonic imaging.

[3]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

[4]  Hemant D. Tagare,et al.  Evaluation of Four Probability Distribution Models for Speckle in Clinical Cardiac Ultrasound Images , 2006, IEEE Transactions on Medical Imaging.

[5]  J. Alison Noble,et al.  2D+T acoustic boundary detection in echocardiography , 2000, Medical Image Anal..

[6]  R. Cobbold Foundations of Biomedical Ultrasound , 2006 .

[7]  Michael G. Strintzis,et al.  Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks , 1994, IEEE Trans. Image Process..

[8]  Michael Brady,et al.  Application of 3D local phase theory in vessel segmentation , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  P M Shankar,et al.  A model for ultrasonic scattering from tissues based on the K distribution. , 1995, Physics in medicine and biology.

[10]  M. Nakagami The m-Distribution—A General Formula of Intensity Distribution of Rapid Fading , 1960 .

[11]  Werner Krischer,et al.  The Data Analysis BriefBook , 1998 .

[12]  James A. Zagzebski,et al.  Essentials Of Ultrasound Physics , 1996 .

[13]  Ali Abdi,et al.  Performance comparison of three different estimators for the Nakagami m parameter using Monte Carlo simulation , 2000, IEEE Communications Letters.

[14]  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.

[15]  Nassir Navab,et al.  The 2D Analytic Signal on RF and B-Mode Ultrasound Images , 2011, IPMI.

[16]  J. Goodman Speckle Phenomena in Optics: Theory and Applications , 2020 .

[17]  Carl-Fredrik Westin,et al.  Accurate Airway Wall Estimation Using Phase Congruency , 2006, MICCAI.

[18]  Gerald Sommer,et al.  Dense Optical Flow Estimation from the Monogenic Curvature Tensor , 2007, SSVM.

[19]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[20]  Michael Felsberg,et al.  The Monogenic Scale-Space: A Unifying Approach to Phase-Based Image Processing in Scale-Space , 2004, Journal of Mathematical Imaging and Vision.

[21]  Robert Rohling,et al.  Bone Segmentation and Fracture Detection in Ultrasound Using 3D Local Phase Features , 2008, MICCAI.

[22]  Gabriella Cincotti,et al.  Frequency decomposition and compounding of ultrasound medical images with wavelet packets , 2001, IEEE Transactions on Medical Imaging.

[23]  Andriy Myronenko,et al.  Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates , 2009, FIMH.

[24]  D. L. Hykes,et al.  Ultrasound Physics and Instrumentation , 1985 .

[25]  J A Noble,et al.  Ultrasound image segmentation and tissue characterization , 2010, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[26]  Michael Brady,et al.  Phase mutual information as a similarity measure for registration , 2005, Medical Image Anal..

[27]  Douglas L. Jones,et al.  Detection of lines and boundaries in speckle images-application to medical ultrasound , 1999, IEEE Transactions on Medical Imaging.

[28]  Nassir Navab,et al.  Locally adaptive Nakagami-based ultrasound similarity measures. , 2012, Ultrasonics.

[29]  C. Zetzsche,et al.  Fundamental limits of linear filters in the visual processing of two-dimensional signals , 1990, Vision Research.

[30]  Jean Meunier,et al.  Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization , 2009, IEEE Transactions on Medical Imaging.

[31]  Song-xi Chen,et al.  Probability Density Function Estimation Using Gamma Kernels , 2000 .

[32]  G. Sommer,et al.  The geometry of 2D image signals , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  David J. Fleet,et al.  Phase-based disparity measurement , 1991, CVGIP Image Underst..

[34]  Gustavo Carneiro,et al.  Phase-Based Local Features , 2002, ECCV.

[35]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[36]  E. Jakeman,et al.  A model for non-Rayleigh sea echo , 1976 .

[37]  Michael Brady,et al.  Spatio-temporal Registration of Real Time 3D Ultrasound to Cardiovascular MR Sequences , 2007, MICCAI.

[38]  Michael Brady,et al.  On the Choice of Band-Pass Quadrature Filters , 2004, Journal of Mathematical Imaging and Vision.

[39]  B. Goldberg,et al.  Classification of ultrasonic B-mode images of breast masses using Nakagami distribution , 2001, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[40]  R. Lord,et al.  THE USE OF THE HANKEL TRANSFORM IN STATISTICS: I. GENERAL THEORY AND EXAMPLES , 1954 .

[41]  J. Alison Noble,et al.  Registration of Multiview Real-Time 3-D Echocardiographic Sequences , 2007, IEEE Transactions on Medical Imaging.

[42]  D. Donoho,et al.  Fast and accurate Polar Fourier transform , 2006 .

[43]  G. Cloutier,et al.  A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. , 2010, Ultrasound in medicine & biology.

[44]  J. Alison Noble,et al.  Nakagami imaging with small windows , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[45]  Michael Brady,et al.  Feature extraction from cancer images using local phase congruency: A reliable source of image descriptors , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[46]  Michael Brady,et al.  Advanced phase-based segmentation of multiple cells from brightfield microscopy images , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[47]  R. Lord THE USE OF THE HANKEL TRANSFORM IN STATISTICS , 1954 .

[48]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .