Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Computers in Biology and Medicine
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
IEEE Transactions on Information Technology in Biomedicine
A new preprocessing approach for cell recognition
IEEE Transactions on Information Technology in Biomedicine
Fault diagnosis of power transformer based on support vector machine with genetic algorithm
Expert Systems with Applications: An International Journal
Multiclass detection of cells in multicontrast composite images
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
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To achieve high throughput with robotic systems based on optical microscopy, it is necessary to replace the human observer with computer vision algorithms that can identify and localize individual cells as well as carry out additional studies on these cells in relation to biochemical parameters. The latter task is best accomplished with the use of fluorescent probes. Since the number of fluorescence channels is limited, it is highly desirable to accomplish the cell identification and localization task with transmitted light microscopy. In previous work, we developed algorithms for automatic detection of unstained cells of a single type in bright field images [X. Long, W.L. Cleveland, Y.L. Yao, A new preprocessing approach for cell recognition, IEEE Transactions on Information Technology in Biomedicine 9 (3) (2005) 407-412; X. Long, W.L. Cleveland, Y.L. Yao, Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure, Computers in Biology and Medicine 36 (2006) 339-362]. Here we extend this technology to facilitate identification and localization of multiple cell types. We formulate the detection of multiple cell types in mixtures as a supervised, multiclass pattern recognition problem and solve it by extension of the Error Correcting Output Coding (ECOC) method to enable probability estimation. The use of probability estimation provides both cell type identification as well as cell localization relative to pixel coordinates. Our approach has been systematically studied under different overlap conditions and outperforms several commonly used methods, primarily due to the reduction of inconsistent labeling by introducing redundancy. Its speed and accuracy are sufficient for use in some practical systems.