Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Moving object segmentation using the flux tensor for biological video microscopy
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Fast graph partitioning active contours for image segmentation using histograms
Journal on Image and Video Processing
Dual Channel Colocalization for Cell Cycle Analysis Using 3D Confocal Microscopy
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Cell cycle progression studies using subcellular markers offer important insight into cellular mechanisms of disease and therapeutic drug development. Due to the large volumes of microscopy data involved in such studies, a manual approach to extracting quantitative information is not only prohibitive but error prone. We present an automatic cell cycle phase identification algorithm applied to 3D spinning disk confocal microscopy imagery of mouse embryonic fibroblast cells. In our training dataset, each 3D image stack depicts a single cell in a manually identified cell phase, and is recorded via two channels showing the fluorescently marked protein PCNA and the chromocenters, respectively. We use a 3D k-means approach to segment each volume and extract a set of shape and curvature features to characterize the subcellular foci patterns associated with cell cycle phases for each channel. Radial features are used to describe the spatial distribution of PCNA over the course of the cell cycle. A support vector machine (SVM) classifier using 234 features was trained and achieved a recognition rate of 83% for the chromocenter and 86% for the PCNA channels separately on the testing data. A combined SVM classifier using both channels and 468 features further improved the accuracy to nearly 92% for five phases (G1, SE, SM, SL, G2) and shows promising scalability.