1. Ultrasound-contrast-agent dispersion and velocity imaging for prostate cancer localization 
Ruud JG van Sloun, Libertario Demi, Arnoud W Postema, Jean JMCH de la Rosette, Hessel Wijkstra, Massimo Mischi 
Medical Image Analysis 
Volume 35, Pages (January 2017)
DOI: /j.media
Copyright © 2016 Elsevier B.V. Terms and Conditions

2. Fig. 1
Kernel for impulse response estimation, showing the Wiener system model w→i between the indicator dilution curve at the center pixel and the ith pixel within the kernel.
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

3. Fig. 2
Eigenvalues λx˜ as well as the Minimum Description Length (MDL) criterion for an example sample-autocorrelation matrix. The value of n that minimizes the MDL is indicated by the dashed vertical line, here being 16.
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

4. Fig. 3
Wiener filter coefficient estimates obtained from a pixel in a benign (a) and a malignant (b) region. The convection–diffusion Green’s functions for least squares (LS) and Maximum Likelihood (ML) parameter estimation are also shown.
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

5. Fig. 4
Two examples of DCE-US frames (a,g), together with the obtained hemodynamic parametric images showing (b,h) the dispersion coefficient D, (c,i) the velocity v, and (d,j) the Péclet number Pe. Plots (e,k) show the maps based on the dispersion-related correlation analysis r (Kuenen et al., 2013a). The corresponding histology slices are shown in (f,l), where malignant tissue is marked in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

6. Fig. 5
The Receiver-Operating-Characteristic (ROC) curves for classification of benign and malignant pixels by the estimated dispersion coefficient (D), velocity (v) and Péclet number (Pe), as obtained using Maximum Likelihood (ML) and Least Squares (LS).
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

7. Fig. 6
The benign and malignant class histograms for the Maximum Likelihood estimates of the velocity (v) and dispersion coefficient (D). The optimal classification thresholds are indicated by a dashed line. A pixel was classified malignant for v > 0.583 mm/s, and for D < 0.350 mm2/s.
Medical Image Analysis  , DOI: ( /j.media )
Copyright © 2016 Elsevier B.V. Terms and Conditions

8. Medical Image Analysis 2017 35, 610-619DOI: (10. 1016/j. media. 2016
Copyright © 2016 Elsevier B.V. Terms and Conditions