Multiclass detection of cells in multicontrast composite images

Authors:
Xi Long;W. Louis Cleveland;Y. Lawrence Yao
Affiliations:
Mechanical Engineering Department, Columbia University, New York, NY 10027, USA;Department of Medicine at St. Luke's-Roosevelt Hospital Center and Columbia University, New York, NY 10019, USA;Mechanical Engineering Department, Columbia University, New York, NY 10027, USA
Venue:
Computers in Biology and Medicine
Year:
2010

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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.