Automated classification of subcellular patterns in multicell images without segmentation into single cells

Fluorescence microscope images capture information from an entire field of view, which often comprises several cells scattered on the slide. We have previously trained classifiers to accurately predict subcellular location patterns by using numerical features calculated from manually cropped 2D single-cell images. We describe here results on directly classifying fields of fluorescence microscope images using a subset of our previous features that do not require segmentation into single cells. Feature selection was conducted by stepwise discriminant analysis (SDA) to select the most discriminative features from the feature set. Better classification performance was achieved on multicell images than single-cell images, suggesting a promising future for classifying subcellular patterns in tissue images.