@article {228, title = {Spectrum sensing in cognitive radio using a Markov-Chain Monte-Carlo scheme}, journal = {IEEE Communications Letters}, volume = {14}, year = {2010}, pages = {830-832}, abstract = {

In this letter, a novel stochastic strategy to spectrum sensing is investigated for the purpose of improving spectrum sensing efficiency of cognitive radio (CR) systems. The problem of selecting the optimal sequence of channels to finely sensing is formulated as an optimization problem to maximize the probability of obtaining available channels, and is then subsequently solved by using a Markov-Chain Monte-Carlo (MCMC) scheme. By employing a nonparametric approach such as the MCMC scheme, the reliance on specific traffic models is alleviated. Experimental results show that the proposed algorithm has the potential to achieve noticeably improved performance in terms of overhead and percentage of missed spectrum opportunities, thus making it well suited for use in CR networks.

}, keywords = {channel allocation, channel selection, cognitive radio, CR systems, Markov chain Monte Carlo scheme, Markov processes, Monte Carlo methods, network traffic, optimisation, optimization problem, spectral analysis, spectrum sensing, telecommunication traffic}, issn = {1089-7798}, doi = {http://dx.doi.org/10.1109/LCOMM.2010.080210.100569}, author = {X Wang and A Wong and P-H Ho} } @inproceedings {289, title = {A novel algorithm for extraction of the layers of the cornea}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

Accurate corneal layer boundary extraction from optical coherence tomograms can provide precise layer thickness measurements required in the analysis of corneal disease. This paper establishes a novel approach to precisely obtain the five primary corneal layer boundaries. The proposed method determines correspondence relationships between the layer boundaries to facilitate robust boundary extraction in the presence of noise and artifacts. The first phase of the method applies morphological operators to enhance the prominent structural features of the cornea. The second phase uses a semi-automated segmentation algorithm to extract the upper and lower boundaries of the cornea; these boundaries are used to register the corneal image. The final phase extracts all five boundaries using a global optimization method exploiting the medial correspondence relationship between each layers. The proposed method is tested and verified using a representative set of optical coherence tomography images and compared against several state of the art methods. The proposed method is demonstrated to be more robust to noise, to provide more accurate segmentation results, and to require fewer user interactions than the other published methods.

}, keywords = {corneal disease, corneal image register, corneal layer boundary extraction, diseases, eye, Feature Extraction, global optimization method, image registration, image segmentation, layer thickness measurements, medical image processing, optical coherence tomography, optical tomography, optimisation, semiautomated segmentation algorithm, thickness measurement}, doi = {http://dx.doi.org/10.1109/CRV.2009.22}, author = {J A. Eichel and A Mishra and D A. Clausi and P Fieguth} } @inproceedings {369, title = {Stem-cell localization: a deconvolution problem}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Hematopoietic Stem Cells (HSCs) form blood and immune cells and are responsible for the constant renewal of blood. To produce new blood cells, HSCs proliferate and differentiate to different blood cell types continuously during their lifetime. Hence they are of substantial interest in stem cell therapy and cancer research. To classify HSCs to different groups, they must be observed/tracked over time and their key features including cell size, shape, and motility must be extracted. The manual tracking is an onerous task and automated methods are in high demand. The first stage of an semi-automatic/automatic tracking system is cell segmentation. In our previous work we addressed the cell segmentation/localization problem. Modelling adjacent or splitting cells is very challenging and our previous methods might fail to accurately model a group of adjacent cells or a splitting cell. In this paper we address this issue and propose a deconvolution method to precisely model individual HSCs as well as adjacent (splitting) HSCs. An optimization algorithm is combined with a template matching method to segment cell regions and locate the cell centers.

}, keywords = {adjacent cells, biomedical optical imaging, blood, cell segmentation, cellular biophysics, deconvolution, deconvolution problem, hematopoietic stem cells, image segmentation, immune cells, medical image processing, optimisation, optimization algorithm, stem-cell localization, template matching method}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353597}, author = {N Nezamoddin Kachouie and P Fieguth and E Jervis} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} }