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