Training based cell detection from bright-field microscope images

This paper proposes a framework for cell detection from bright-field microscope images. The method is trained using manually annotated images, and it uses Support Vector Machine classifiers with Histogram of Oriented Gradient features. The performance of the method is evaluated using 16 training and 12 test images with altogether 10736 human prostate cancer cells. Both the implementation and the annotated image database are released for download. The experiments consider various parameters and their effect on performance, and reaches accurate detection results with cross-validated AUC over 0.98, and mean relative deviation of 9 % from manually counted annotations in the growth curve over six days.