@inproceedings {789,
	title = {Average cell orientation, shape and size estimated from tissue images},
	booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision},
	year = {2005},
	month = {05/2005},
	address = {Victoria, B.C., Canada},
	abstract = {<p>Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical \&$\#$39;composite cell\&$\#$39; are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.</p>
},
	keywords = {area moments, average cell orientation, average cell shape, average cell size, biological tissue, biological tissues, biology computing, biomechanical tissue models, cell boundary visibility, cell geometry, cell pigmentation, Computer Vision, edge detection, elliptical composite cell, embryology, Gabor filters, least squares ellipse fitting, magnitude spatial-frequency domain, medical image processing, morphogenesis, shape detection, simple edge detection, statistical analysis, tissue images},
	doi = {http://dx.doi.org/10.1109/CRV.2005.22},
	author = {P Iles and D A. Clausi and S Puddister and G W. Brodland}
}
@inproceedings {446,
	title = {Estimation of average cell shape from digital images of cellular surfaces},
	booktitle = {1st Canadian Conference on Computer and Robot Vision},
	year = {2004},
	month = {05/2004},
	address = {London, Ontario, Canada},
	abstract = {<p>A method is proposed to determine the average cellular geometry in high-resolution images of embryonic epithelia. The concept of a \&lsquo;composite cell\&rsquo; is used to represent the average cell shape. This composite cell can provide evidence of stresses present in the epithelia. A new adaptive contrast enhancement routine is applied to the input image first, followed by an iterative watershed segmentation. The composite cell is calculated from the segmentation results. Qualitative results show that this computationally inexpensive algorithm produces accurate results for a variety of image sizes, contrast levels, cell shapes and appearances.</p>
},
	keywords = {adaptive contrast enhancement, composite cell, shape detection, watershed segmentation},
	doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301455},
	author = {P Iles and D A. Clausi and G W. Brodland}
}
