Introduction to support vector learning
Advances in kernel methods
Cell image segmentation for diagnostic pathology
Advanced algorithmic approaches to medical image segmentation
Model-based Segmentation of Leukocytes Clusters
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Method for Error Rejection in Multiple Classifier Systems
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
A texture approach to leukocyte recognition
Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
Support Vector Machines Applied to White Blood Cell Recognition
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Segmentation of moving cells in bright field and epi-fluorescent microscopic image sequences
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
| Hi-index | 0.00 |
Automated leukocyte detection, segmentation, and classification is an important task in clinical diagnosis. In this paper we present an approach to leukocyte cytoplasm and nucleus segmentation that is robust with respect to image quality and cell appearance. Cell properties are described by a set of statistical color and shape features. Pairwise coupling of SVM classification results is used to determine cell type probabilities. Evaluation of the method on a set of 1166 images containing 13 different cell types has resulted in 95% correctly segmented cells and a classification accuracy of 88% (at 20% reject rate).