Affine and projective active contour models
Pattern Recognition
Joint co-clustering: Co-clustering of genomic and clinical bioimaging data
Computers & Mathematics with Applications
Particle measurement in scanning electron microscopy images
Machine Graphics & Vision International Journal
Automatic Tracking of Escherichia Coli Bacteria
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Intuitive Visualization and Querying of Cell Motion
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Effective segmentation and classification for HCC biopsy images
Pattern Recognition
Topology adaptive active membrane
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Automated segmentation of tissue images for computerized IHC analysis
Computer Methods and Programs in Biomedicine
A modified support vector machine and its application to image segmentation
Image and Vision Computing
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
Tracking by means of geodesic region models applied to multidimensional and complex medical images
Computer Vision and Image Understanding
Rapidly adaptive cell detection using transfer learning with a global parameter
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
An object tracking scheme based on local density
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Cell segmentation using coupled level sets and graph-vertex coloring
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Robust tracking of migrating cells using four-color level set segmentation
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Combined segmentation and tracking of neural stem-cells
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Segmentation of nanocolumnar crystals from microscopic images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
Computer-aided techniques for chromogenic immunohistochemistry: Status and directions
Computers in Biology and Medicine
Cell cycle phase detection with cell deformation analysis
Expert Systems with Applications: An International Journal
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We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.