THESIS
2014
xvi, 85 pages : illustrations (some color) ; 30 cm
Abstract
Tissue deformation analysis aims at studying the elastic properties of soft tissues.
Such properties provide unique information for clinical diagnosis. In order to measure
the elastic properties of tissues, a source of mechanical vibration is needed to induce tissue
motion and an imaging modality is used to record images of tissues. Motion tracking
methods are then applied to infer the tissue motion / deformation. In this thesis, ultrasound
image based tissue deformation analysis is studied. A difficult problem called
feature-motion decorrelation, which restricts the effectiveness of the tracking methods,
is solved using a coupled filtering method and an affine warping method. The performance
of both methods is evaluated with a comparison with the direct correlation
method, whi...[
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Tissue deformation analysis aims at studying the elastic properties of soft tissues.
Such properties provide unique information for clinical diagnosis. In order to measure
the elastic properties of tissues, a source of mechanical vibration is needed to induce tissue
motion and an imaging modality is used to record images of tissues. Motion tracking
methods are then applied to infer the tissue motion / deformation. In this thesis, ultrasound
image based tissue deformation analysis is studied. A difficult problem called
feature-motion decorrelation, which restricts the effectiveness of the tracking methods,
is solved using a coupled filtering method and an affine warping method. The performance
of both methods is evaluated with a comparison with the direct correlation
method, which does not facilitate any compensation of decorrelation. Results show that
the coupled filtering method and the affine warping method are able to achieve a robust
deformation estimation even when tissues undergo extremely large deformation, while
the direct correlation method fails when tissue deformation is large. The performance
of the affine warping method is also evaluated using B-mode (BM) images instead of
radio-frequency (RF) signals, and the coupled filtering method and the affine warping
method are compared with one of the state-of-the-art methods in ultrasound based tissue
deformation analysis. Tracking methods that only model translation, compression and
expansion as in most of the state-of-the-art methods will fail in a typical elastography
setting with multiple stiffness regions in tissues.
Ultrasound based image registration, a problem similar to the problem of ultrasound
based motion analysis, is also studied. In particular, a groupwise registration method
is proposed for 3-D echocardiography. The proposed method aligns 3-D ultrasound
image volumes of the heart from different view angles. After the alignment, image
information from multiple sequences can be incorporated for further processing. Sparse
and low rank modeling of ultrasound image volumes is used. The alignment of image
volumes is facilitated by finding a minimum number of linear bases to approximate all
volumes. The linearly correlated approximations serve as the fused volumes and in the
fused volumes speckle patterns are reduced. The fused volumes serve as the reference
for the registration and in the proposed method no reference image volumes need to be
chosen. By using the proposed method, missing structures in single-view volumes can
be recovered by modeling them as sparse outliers.
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