Abstract-Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-densitybased image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.Index Terms-Arterial wall segmentation, calcification detection, intensity prior, intravascular ultrasound (IVUS), lumen segmentation, media adventitia segmentation, model-based segmentation, side branch detection, shape prior.
Magnetic resonance imaging (MRI) reconstruction from sparsely sampled data has been a difficult problem in medical imaging field. We approach this problem by formulating a cost functional that includes a constraint term that is imposed by the raw measurement data in k-space and the L 1 norm of a sparse representation of the reconstructed image. The sparse representation is usually realized by total variational regularization and/or wavelet transform. We have applied the Bregman iteration to minimize this functional to recover finer scales in our recent work. Here we propose nonlinear inverse scale space methods in addition to the iterative refinement procedure. Numerical results from the two methods are presented and it shows that the nonlinear inverse scale space method is a more efficient algorithm than the iterated refinement method.
In this paper, we present a new method for ultrasound image registration. For each image to be registered, our method first applies an ultrasound-specific information-theoretic feature detector, which is based on statistical modeling of speckle and provides a feature image that robustly delineates important edges in the image. These feature images are then registered using differential equations, the solution of which provides a locally optimal transformation that brings the images into alignment. We describe our method and present experimental results demonstrating its effectiveness, particularly for low contrast, speckled images. Furthermore, we compare our method to standard gradient-based techniques, which we show are more susceptible to misregistration.
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