@article {106, title = {Sparse reconstruction of breast MRI using homotopic L0 minimization in a regional sparsified domain}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {60}, year = {2013}, pages = {743 - 752}, abstract = {

The use of magnetic resonance imaging (MRI) for early breast examination and screening of asymptomatic women has become increasing popular, given its ability to provide detailed tissue characteristics that cannot be obtained using other imaging modalities such as mammography and ultrasound. Recent application-oriented developments in compressed sensing theory have shown that certain types of magnetic resonance images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled k-space data below Nyquist sampling rates using homotopic L0 minimization schemes, which holds great potential for significantly reducing acquisition time. An important consideration in the use of such homotopic L0 minimization schemes is the choice of sparsifying transform. In this work, a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI. By taking local regional characteristics into account, the regional differential sparsifying transform can better account for signal variations and fine details that are characteristic of breast MRI than the popular finite differential transform, while still maintaining strong structure fidelity. Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods.

}, keywords = {breast, magnetic resonance imaging, minimization, MRI, reconstruction, regional differential transform}, issn = {0018-9294}, doi = {10.1109/TBME.2010.2089456}, author = {A Wong and A Mishra and P Fieguth and D A. Clausi} } @inproceedings {236, title = {Automated 3D reconstruction and segmentation from optical coherence tomography}, booktitle = {11th European Conference on Computer Vision}, year = {2010}, month = {09/2010}, address = {Crete, Greece}, abstract = {

Ultra-High Resolution Optical Coherence Tomography is a novel imaging technology that allows non-invasive, high speed, cellular resolution imaging of anatomical structures in the human eye, including the retina and the cornea.

\ A three-dimensional study of the cornea, for example, requires the segmentation and mutual alignment of a large number of two-dimensional images. Such segmentation has, until now, only been undertaken by hand for individual two-dimensional images; this paper presents a method for automated segmentation, opening substantial opportunities for 3D corneal imaging and analysis, using many hundreds of 2D slices.

}, keywords = {cornea, non-invasive imaging, OCT, reconstruction, Segmentation, statistical modelling, UHROCT}, isbn = {3-642-15557-X, 978-3-642-15557-4}, author = {J A. Eichel and K Bizheva and D A. Clausi and P Fieguth} } @article {576, title = {Multiscale statistical methods for the segmentation of signals and images}, journal = {IEEE Transactions on Image Processing}, volume = {9}, year = {2000}, pages = {456 - 468}, abstract = {

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired

}, keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.826782}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} }