Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation
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) 407412; 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) 339362]. 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.
cell detection support vector machines error correcting output coding (ecoc) multiclass classification