1,201 research outputs found
Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles
We investigate the usefulness of quantitative ultrasound (QUS) and B-mode
texture features for characterization of ulnar nerve fascicles. Ultrasound data
were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the
nerves were extracted to prepare histology sections. 85 fascicles were matched
between the B-mode images and the histology sections. For each fascicle image,
we selected an intra-fascicular region of interest. We used histology sections
to determine features related to the concentration of collagen and myelin, and
ultrasound data to calculate backscatter coefficient (-24.89 dB 8.31),
attenuation coefficient (0.92 db/cm-MHz 0.04), Nakagami parameter (1.01
0.18) and entropy (6.92 0.83), as well as B-mode texture features
obtained via the gray level co-occurrence matrix algorithm. Significant
Spearman's rank correlations between the combined collagen and myelin
concentrations were obtained for the backscatter coefficient (R=-0.68), entropy
(R=-0.51), and for several texture features. Our study demonstrates that QUS
may potentially provide information on structural components of nerve
fascicles
Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound
Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation
Xampling in Ultrasound Imaging
Recent developments of new medical treatment techniques put challenging
demands on ultrasound imaging systems in terms of both image quality and raw
data size. Traditional sampling methods result in very large amounts of data,
thus, increasing demands on processing hardware and limiting the exibility in
the post-processing stages. In this paper, we apply Compressed Sensing (CS)
techniques to analog ultrasound signals, following the recently developed
Xampling framework. The result is a system with significantly reduced sampling
rates which, in turn, means significantly reduced data size while maintaining
the quality of the resulting images.Comment: 17 pages, 9 Figures. Introduced in SPIE Medical Imaging Conference,
Orlando Florida, 201
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regularity ; Second, estimation of local regularity is based on new multiscale
quantities referred to as wavelet leaders ; Third, segmentation from local
regularity faces a fundamental bias variance trade-off: In nature, local
regularity estimation shows high variability that impairs the detection of
changes, while a posteriori smoothing of regularity estimates precludes from
locating correctly changes. Instead, the present contribution proposes several
variational problem formulations based on total variation and proximal
resolutions that effectively circumvent this trade-off. Estimation and
segmentation performance for the proposed procedures are quantified and
compared on synthetic as well as on real-world textures
Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography
Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to construct the elastic distributions consistent with the obtained strains on observation points. This thesis focuses on the former problem whose solution is used as an input to the latter problem. The aim is to provide the inverse problem community with accurate strain estimates of a coronary artery vessel wall. In doing so, a new ultrasonic image-based elastography approach is developed. Because the accuracy and quality of the estimated strain fields depend on the resolution level of the ultrasound image and to date best resolution levels obtained in the literature are not enough to clearly see all boundaries of the artery, one of the main goals is to acquire high-resolution coronary vessel wall ultrasound images at different pressures. For this purpose, first an experimental setup is designed to collect radio frequency (RF) signals, and then image formation algorithm is developed to obtain ultrasound images from the collected signals. To segment the noisy ultrasound images formed, a geodesic active contour-based segmentation algorithm with a novel stopping function that includes local phase of the image is developed. Then, region-based information is added to make the segmentation more robust to noise. Finally, elliptical deformable template is applied so that a priori information regarding the shape of the arteries could be taken into account, resulting in more stable and accurate results. The use of this template also implicitly provides boundary point correspondences from which high-resolution, size-independent, non-rigid and local strain fields of the coronary vessel wall are obtained.Ph.D.Committee Chair: Benkeser, Paul; Committee Member: Koblasz, Arthur; Committee Member: Skrinjar, Oskar; Committee Member: Vidakovic, Brani; Committee Member: Yezzi, Anthon
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