@article {176, title = {Quasi-Monte Carlo estimation approach for denoising MRI data based on regional statistics}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {58}, year = {2011}, month = {1076 - 1083}, abstract = {

An important postprocessing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved using a quasi-Monte Carlo method that takes into account the statistical characteristics of the underlying noise and the regional statistics of the observed signal in a data-adaptive manner. A set of experiments were per formed to compare the proposed quasi-Monte Carlo estimation (QMCE) method to state-of-the-art wavelet-based MR noise reduction (WAVE) and nonlocal means MR noise reduction (NLM) methods using MR data volumes with synthetic noise, as well as real noise-contaminated MR data. Experimental results show that QMCE is capable of achieving state-of-the-art performance when compared to WAVE and NLM methods quantitatively in SNR, mean structural similarity (MSSIM), and contrast measures. Visual comparisons show that QMCE provides effective noise suppression, while better preserving tissue structural boundaries and restoring contrast.

}, keywords = {Bayes methods, biomedical MRI, contrast measures, data adaptive method, general Bayesian least-squares estimation, Image Denoising, mean structural similarity, medical image processing, Monte Carlo methods, MR data post processing step, noise-free signal, nonlocal means MR noise reduction, QMCE, quasi Monte Carlo estimation approach, regional statistics, stochastic approach, stochastic processes, tissue structural boundaries, WAVE, wavelet transforms, wavelet-based MR noise reduction}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2010.2048325}, author = {A Wong and A Mishra} } @article {177, title = {Quasi-Monte Carlo estimation approach for denoising MRI data based on regional statistics}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {58}, year = {2011}, month = {1076 - 1083}, abstract = {

An important postprocessing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved using a quasi-Monte Carlo method that takes into account the statistical characteristics of the underlying noise and the regional statistics of the observed signal in a data-adaptive manner. A set of experiments were per formed to compare the proposed quasi-Monte Carlo estimation (QMCE) method to state-of-the-art wavelet-based MR noise reduction (WAVE) and nonlocal means MR noise reduction (NLM) methods using MR data volumes with synthetic noise, as well as real noise-contaminated MR data. Experimental results show that QMCE is capable of achieving state-of-the-art performance when compared to WAVE and NLM methods quantitatively in SNR, mean structural similarity (MSSIM), and contrast measures. Visual comparisons show that QMCE provides effective noise suppression, while better preserving tissue structural boundaries and restoring contrast.

}, keywords = {Bayes methods, biomedical MRI, contrast measures, data adaptive method, general Bayesian least-squares estimation, Image Denoising, mean structural similarity, medical image processing, Monte Carlo methods, MR data post processing step, noise-free signal, nonlocal means MR noise reduction, QMCE, quasi Monte Carlo estimation approach, regional statistics, stochastic approach, stochastic processes, tissue structural boundaries, WAVE, wavelet transforms, wavelet-based MR noise reduction}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2010.2048325}, author = {A Wong and A Mishra} } @inproceedings {362, title = {SAR sea ice image segmentation based on edge-preserving watersheds}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.

}, keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2007.58}, author = {X Yang and D A. Clausi} }