Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast limited adaptive histogram equalization
Graphics gems IV
Double exponential smoothing: an alternative to Kalman filter-based predictive tracking
EGVE '03 Proceedings of the workshop on Virtual environments 2003
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
| Hi-index | 0.00 |
The zebrafish embryo is a common model organism for cardiac development and genetics. However, the current method of analyzing the embryo heart images is still mainly the manual and visual inspection through the microscope by scoring embryos visually - a very laborious and expensive task for the biologist. We propose to automatically segment the embryo cardiac chambers from fluorescent microscopic video sequences, allowing morphological and functional quantitative features of cardiac activity to be extracted. Several methods are presented and compared within a large range of images, varying in quality, acquisition parameters, and embryos position. Despite such variability in the images, the best method reaches a 70% of accuracy, allowing reducing biologists workload by automating some of the tedious manual segmentation tasks.