Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Coupled Minimum-Cost Flow Cell Tracking
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Conditional random fields for object and background estimation in fluorescence video-microscopy
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
3D spatial drift correction using Kalman filtering for fluorescence based imaging
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Identifying fusion events in fluorescence microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Using physics engines to track objects in images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Multiple hypothesis tracking in microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Cell population tracking and lineage construction with spatiotemporal context
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Particle tracking in fluorescent microscopy images improved by morphological source separation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Microtubule dynamics analysis using kymographs and variable-rate particle filters
IEEE Transactions on Image Processing
Optimal live cell tracking for cell cycle study using time-lapse fluorescent microscopy images
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Optimal-flow minimum-cost correspondence assignment in particle flow tracking
Computer Vision and Image Understanding
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Tracking growing axons by particle filtering in 3d+t fluorescent two-photon microscopy images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
A multiple hypothesis based method for particle tracking and its extension for cell segmentation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.