title: Multiclass detection of cells in multicontrast composite images.
abstract: In this paper, we describe a framework for multiclass cell detection in composite images consisting of images obtained with three different contrast methods for transmitted light illumination (referred to as multicontrast composite images). Compared to previous multiclass cell detection results [1], the use of multicontrast composite images was found to improve the detection accuracy by introducing more discriminatory information into the system. Preprocessing multicontrast composite images with Kernel PCA was found to be superior to traditional linear PCA preprocessing, especially in difficult classification scenarios where high-order nonlinear correlations are expected to be important. Systematic study of our approach under different overlap conditions suggests that it possesses sufficient speed and accuracy for use in some practical systems.
authors.affiliation: Mechanical Engineering Department, Columbia University, New York, NY 10027, USA.
authors.forename: Xi
authors.initials: X
authors.lastname: Long
authors.forename: W Louis
authors.initials: WL
authors.lastname: Cleveland
authors.forename: Y Lawrence
authors.initials: YL
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D045744:Cell Line, Tumor; D005057:Eukaryotic Cells / Q000166:cytology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012372:ROC Curve; D012984:Software; D013194:Staining and Labeling