@inproceedings {361, title = {Automated detection of mitosis in embryonic tissues}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2007}, month = {05/2007}, address = {Montreal, Quebec, Canada}, abstract = {

Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos. A new image processing algorithm that does not rely on the use of contrast agents was developed to detect mitosis in embryonic tissue. Unlike previous approaches that uses still images, the algorithm presented here uses temporal information from time-lapse images to track the deformation of the embryonic tissue and then uses changes in intensity at tracked regions to identify the locations of mitosis. On a one hundred minute image sequence, consisting of twenty images, the algorithm successfully detected eighty-one out of the ninety-five mitosis. The performance of the algorithm is calculated using the geometric mean measure as 82\%. Since no other method to count mitoses in live tissues is known, comparisons with the present results could not be made.

}, keywords = {automated mitosis detection, cancer, early stage embryo, embryonic tissues, image processing algorithm, image sequence, image sequences, medical image processing, time-lapse image}, doi = {http://dx.doi.org/10.1109/CRV.2007.11}, author = {P Siva and G W. Brodland and D A. Clausi} } @inproceedings {366, title = {A medical texture local binary pattern for TRUS prostate segmentation}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.

}, keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {749, title = {An elliptical level set method for automatic TRUS prostate image segmentation}, booktitle = {IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

One of the most important tasks in prostate cancer diagnosis and treatment is segmentation of transrectal ultrasound (TRUS) prostate images. Due to the large volumes of TRUS prostate images, automatic segmentation systems are mandatory. Weak prostate boundaries, speckle noise and the short range of gray levels make the task more challenging and difficult. Deformable models have been considered as an effective approach for semi-automatic prostate segmentation. However the main problem toward a fully automatic segmentation system using deformable models is initialization of seed or control points. In this paper an automatic level set prostate segmentation is presented. A classification method is employed to locate the approximate location of the prostate which is used to initiate the proposed elliptical level set contour. The deformations of the level set are guided by a velocity function which is derived using the TRUS prostate image histogram

}, keywords = {automatic level set prostate segmentation, automatic TRUS prostate image segmentation, biomedical ultrasonics, cancer, classification method, elliptical level set method, image classification, image segmentation, medical image processing, prostate cancer diagnosis, speckle noise, transrectal ultrasound, velocity function, weak prostate boundaries}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270795}, author = {N Kachouie and N Nezamoddin and P Fieguth and S Rahnamayan} } @inproceedings {408, title = {A statistical thresholding method for cell tracking}, booktitle = {2006 IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

Tracking the motion of cells in culture is a task, which often still Is undertaken manually, and for which automated methods are strongly desirable. Researchers visually perform cell motion analysis, observe cell movements and cell shape changes for hours to discover when, where and how fast It moves, splits or dies. Hematopoletlc stem cells (HSCs) proliferate and differentiate to different blood cell types continuously during their lifetime, and are of substantial interest in gene therapy, cancer, and stem-cell research. In this paper a statistical method is introduced to track HSCs over time. A statistical thresholding method is combined with joint probabilistic data association in the proposed HSC tracker

}, keywords = {blood, blood cell, cancer, cell motion analysis, cell tracking, cellular biophysics, gene therapy, hematopoletlc stem cells proliferate, image motion analysis, image segmentation, medical image processing, statistical analysis, statistical thresholding method, stem-cell research}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270801}, author = {J Ramunas and N Nezamoddin Kachouie and P Fieguth and E Jervis} }