@article {269, title = {Alignment of confocal scanning laser ophthalmoscopy photoreceptor images at different polarizations using complex phase relationships}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {56}, year = {2010}, abstract = {

A polarimetric technique for enhancing fundus images was recently introduced , where confocal scanning laser ophthalmoscopy (CSLO) images are acquired under differing incoming polarization states, and spatially resolved Mueller images are constructed based on the images. An important stage in this technique is the alignment of CSLO images acquired under differing polarization states. This has proven to be particularly difficult when dealing with photoreceptor images, which are characterized by poor SNRs and intensity inhomogeneities due to polarization properties. In this paper, an automated approach to aligning CSLO photoreceptor images acquired under differing polarization states is presented. A novel energy functional based on complex phase relationships is introduced that is invariant to polarization and scale, as well as robust to noise and highly sensitive to photoreceptor structural characteristics. A sequential quadratic programming approach is employed to determine the optimal alignment between the photoreceptor images by minimizing the proposed energy functional. The method has been tested on CSLO fish photoreceptor images acquired under differing polarization states and evaluated based on alignment accuracy. The results demonstrate that the proposed method outperforms existing techniques used for aligning CSLO images, with lower mean alignment error for all test cases.

}, keywords = {Algorithms, Animals, bio-optics, biomedical optical imaging, complex phase relationship, Computer-Assisted, Confocal, confocal scanning laser ophthalmoscopy, enhancing fundus image, eye, Fishes, Fundus Oculi, image enhancement, image processing, incoming polarization states, laser applications in medicine, light polarisation, medical image processing, Microscopy, Ophthalmoscopy, Photoreceptor Cells, photoreceptor image, polarimetry, Polarization, quadratic programming, sequential quadratic programming, spatially resolved Mueller image construction, Thermodynamics, Vertebrate}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2009.2017510}, author = {A Wong} } @article {272, title = {Intervertebral disc segmentation and volumetric reconstruction from peripheral quantitative computed tomography imaging}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {56}, year = {2010}, pages = {2748 - 2751}, abstract = {

An automatic system for segmenting and constructing volumetric representations of excised intervertebral discs from peripheral quantitative computed tomography (PQCT) imagery is presented. The system is designed to allow for automatic quantitative analysis of progressive herniation damage to the intervertebral discs under flexion/extension motions combined with a compressive load. Automatic segmentation and volumetric reconstruction of intervertebral disc from PQCT imagery is a very challenging problem due to factors such as streak artifacts and unclear material density separation between contrasted intervertebral disc and surrounding bone in the PQCT imagery, as well as the formation of multiple contrasted regions under axial scans. To address these factors, a novel multiscale level set approach based on the Mumford-Shaft energy functional in iterative bilateral scale space is employed to segment the intervertebral disc regions from the PQCT imagery. A Delaunay triangulation is then performed based on the set of points associated with the intervertebral disc regions to construct the volumetric representation of the intervertebral disc. Experimental results show that the proposed system achieves segmentation and volumetric reconstructions of intervertebral discs with mean absolute distance error below 0.8 mm when compared to ground truth measurements. The proposed system is currently in operational use as a visualization tool for studying progressive intervertebral disc damage.

}, keywords = {Animals, Artificial Intelligence, Automated, automatic quantitative analysis, Computer-Assisted, computerised tomography, Delaunay triangulation, flexion-extension motions, image reconstruction, image segmentation, Imaging, intervertebral disc segmentation, Intervertebral Disk, Intervertebral Disk Displacement, iterative bilateral scale space, material density separation, medical image processing, Mumford-Shah energy functional, Pattern Recognition, peripheral quantitative computed tomography imaging, progressive herniation damage, Radiographic Image Enhancement, Radiographic Image Interpretation, Swine, Three-Dimensional, Tomography, volumetric reconstruction, X-Ray Computed}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2009.2027225}, author = {A Wong and A Mishra and J Yates and P Fieguth and D A. Clausi and J Callaghan} } @article {356, title = {Extended-Hungarian-JPDA: Exact single-frame stem cell tracking}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {54}, year = {2007}, pages = {2011 - 2019}, abstract = {

The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.

}, keywords = {Algorithms, Animals, biocellular images, bioinformatics, biological techniques, biotechnology, cell motility, cell shape, cell size, Cells, cellular biophysics, Computer-Assisted, Data Interpretation, extended-Hungarian-joint probabilistic data association, hematopoietic stem cells, Humans, image enhancement, image interpretation, linear programming optimization methods, Microscopy, multitarget tracking, nonGaussian measurements, nonlinear dynamics, optimal joint probabilistic data association, principal component analysis, Reproducibility of Results, Sensitivity and Specificity, single-frame scan-back stem cell tracking, Statistical, Video}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2007.895747}, author = {N Nezamoddin Kachouie and P Fieguth} }