@article {230, title = {Constrained watershed method to infer morphology of mammalian cells in microscopic images}, journal = {Cytometry Part A}, volume = {77A}, year = {2010}, pages = {1148 - 1159}, abstract = {

Abstract Precise information about the size, shape, temporal dynamics, and spatial distribution of cells is beneficial for the understanding of cell behavior and may play a key role in drug development, regenerative medicine, and disease research. The traditional method of manual observation and measurement of cells from microscopic images is tedious, expensive, and time consuming. Thus, automated methods are in high demand, especially given the increasing quantity of cell data being collected. In this article, an automated method to measure cell morphology from microscopic images is proposed to outline the boundaries of individual hematopoietic stem cells (HSCs). The proposed method outlines the cell regions using a constrained watershed method which is derived as an inverse problem. The experimental results generated by applying the proposed method to different HSC image sequences showed robust performance to detect and segment individual and dividing cells. The performance of the proposed method for individual cell segmentation for single frame high-resolution images was more than 97\%, and decreased slightly to 90\% for low-resolution multiframe stitched images. \© 2010 International Society for Advancement of Cytometry

}, keywords = {automated cell shape analysis, biomedical image analysis, cell uropodia, hematopoietic stem cell, inverse problem, microscopic image sequence, watershed segmentation}, issn = {1552-4930}, doi = {10.1002/cyto.a.20951}, url = {http://dx.doi.org/10.1002/cyto.a.20951}, author = {N. Nezamoddin Kachouie and Z Ezziane and P Fieguth and E Jervis and D Gamble and A Khademhosseini} } @inproceedings {439, 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 = {

A method is proposed to determine the average cellular geometry in high-resolution images of embryonic epithelia. The concept of a \‘composite cell\’ 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.

}, keywords = {adaptive contrast enhancement, composite cell, detection, watershed segmentation}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301455}, author = {P Iles and D A. Clausi 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 = {

A method is proposed to determine the average cellular geometry in high-resolution images of embryonic epithelia. The concept of a \‘composite cell\’ 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.

}, 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} }