Compensation of in-plane rigid motion for in vivo intracoronary ultrasound image sequence

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

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Two approaches to motion analysis of the ultrasound image sequence of carotid atheromatous plaque

Ultrasonics ◽

10.1016/0041-624x(93)90041-w ◽

1993 ◽

Vol 31 (2) ◽

pp. 117-123 ◽

Cited By ~ 14

Author(s):

K.L. Chan

Keyword(s):

Motion Analysis ◽

Ultrasound Image ◽

Image Sequence ◽

Atheromatous Plaque

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Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging

Applied Sciences ◽

10.3390/app8122558 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2558 ◽

Cited By ~ 1

Author(s):

Bin Gao ◽

Shaozhang Xiao ◽

Li Zhao ◽

Xian Liu ◽

Kegang Pan

Keyword(s):

Image Reconstruction ◽

Splitting Method ◽

Ultrasound Image ◽

Synthetic Data ◽

Quadratic Term ◽

Ultrasound Images ◽

Closed Form Solutions ◽

Medical Ultrasound Imaging ◽

Partial Linearization

The compressive deconvolution (CD) problem represents a class of efficient models that is appealing in high-resolution ultrasound image reconstruction. In this paper, we focus on designing an improved CD method based on the framework of a strictly contractive Peaceman–Rechford splitting method (sc-PRSM). By fully excavating the special structure of ultrasound image reconstruction, the improved CD method is easier to implement by partially linearizing the quadratic term of subproblems in the CD problem. The resulting subproblems can obtain closed-form solutions. The convergence of the improved CD method with partial linearization is guaranteed by employing a customized relaxation factor. We establish the global convergence for the new method. The performance of the method is verified via several experiments implemented in realistic synthetic data and in vivo ultrasound images.

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