title: A spectral k-means approach to bright-field cell image segmentation.
abstract: Automatic segmentation of bright-field cell images is important to cell biologists, but difficult to complete due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). Standard approaches such as level set segmentation and active contours work well for fluorescent images where cells appear as round shape, but become less effective when optical artifacts such as halo exist in bright-field images. In this paper, we present a robust segmentation method which combines the spectral and k-means clustering techniques to locate cells in bright-field images. This approach models an image as a matrix graph and segment different regions of the image by computing the appropriate eigenvectors of the matrix graph and using the k-means algorithm. We illustrate the effectiveness of the method by segmentation results of C2C12 (muscle) cells in bright-field images.
authors.affiliation: Computational Mathematics, University of Waterloo, Ontario, Canada. ldbradbury@uwaterloo.ca
authors.forename: Laura
authors.initials: L
authors.lastname: Bradbury
authors.forename: Justin W L
authors.initials: JW
authors.lastname: Wan
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002460:Cell Line; D058948:Cell Tracking / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D051379:Mice; D018485:Muscle Fibers, Skeletal / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique