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Murphy Lab - Classification of Fluorescence Images


Michael V. Boland, former graduate student in Biomedical Engineering
Mia K. Markey, former undergraduate in Biological Sciences

Introduction

The goal of this work is to develop methods that allow the numerical description and subsequent classification of the patterns characteristic of subcellular structures in fluorescence microscope images of eukaryotic cells. Such data are generated on a regular basis by labeling one or more cellular molecules with fluorescent dyes (most often by using antibodies against specific proteins.) As currently practiced, investigators identify patterns based on experience or via comparison with patterns of known proteins. The question we address is whether these patterns can be described in a way that is amenable to further processing by a computer, thereby enabling automation of their analysis.

Approach

There are three steps involved in the automated classification of fluorescence microscope images:

  1. Image acquisition
  2. Feature extraction
  3. Classification
As such, we first generated a set of images that could be used with the subsequently designed classification methods. This set consisted of more than 300 images generated using five different fluorescent markers (four antibodies to cellular proteins and a DNA stain). These images are available for download.

We then proceeded to generate numeric features to describe these images. Because of the nature of fluorescence microscope images, we pursued features that were invariant to translations and rotations of the fluorescence patterns within the images. We also made every effort to choose features without considering the five classes of patterns we had generated. That is, we desired those features that were generic enough to be useful with additional patterns we would generate in the future rather than those tailored to our existing data. The first two sets of features generated, then, were based on Zernike moments and Haralick's texture features.

To classify these features, we implemented both a classification tree and a backpropagation neural network.

Results

The combination of Haralick's texture features and the neural network classifier allowed us to achieve 88% recognition on previously unseen single images from the five classes described above.

Conclusions

Given the significant heterogeneity in the images in each of the five classes we generated, we consider our classification results to be quite good. Furthermore, we believe this result can be improved by looking at many (i.e. 10-20) images of cells subject to identical preparation and then generating a single classification for the entire set (see References for details.)

References

Boland, M.V., Markey, M.K., Murphy, R.F. 1998. Automated Recognition of Patterns Characteristic of Subcellular Structures in Fluorescence Microscopy Images. Cytometry 33:366-375.




Last Updated: 01 Dec 2004




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