Random subwindows and extremely randomized trees for image classification in cell biology.
BACKGROUND
With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks.


RESULTS
We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose.


CONCLUSIONS
Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems.
Marée
Raphaël
R
GIGA Bioinformatics Platform, University of Liege, Liege, Belgium. Raphael.Maree@ulg.ac.be
Geurts
Pierre
P
Wehenkel
Louis
L
D000465:Algorithms; D003628:Database Management Systems; D004912:Erythrocytes; D006367:HeLa Cells; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D012163:Retinal Detachment; D012984:Software; D013672:Technology