@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 = {<p>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.</p>
},
	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}
}
@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 = {<p>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.</p>
},
	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}
}
