Random subwindows and extremely randomized trees for image classification in cell biology

BackgroundWith 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.ResultsWe 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.ConclusionOur 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.

[1]  R Grebe,et al.  Automatic shape quantification of freely suspended red blood cells by isodensity contour tracing and tangent counting. , 1989, Computer methods and programs in biomedicine.

[2]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Louis Wehenkel,et al.  Automatic Learning Techniques in Power Systems , 1997 .

[4]  M V Boland,et al.  Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. , 1998, Cytometry.

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  A. Poustka,et al.  Systematic subcellular localization of novel proteins identified by large‐scale cDNA sequencing , 2000, EMBO reports.

[7]  Robert F. Murphy,et al.  Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images , 2000, ISMB.

[8]  Hermann Ney,et al.  Automatic Classification of Red Blood Cells Using Gaussian Mixture Densities , 2000, Bildverarbeitung für die Medizin.

[9]  Hermann Ney,et al.  Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method' , 2001, Multiple Classifier Systems.

[10]  Robert F. Murphy,et al.  A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..

[11]  Hermann Ney,et al.  Invariant Classification of Red Blood Cells , 2001 .

[12]  John C Reed,et al.  Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools , 2002, Journal of cellular biochemistry. Supplement.

[13]  Robert F. Murphy,et al.  Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images , 2003, J. VLSI Signal Process..

[14]  Stepán Obdrzálek,et al.  Object recognition methods based on transformation covariant features , 2004, 2004 12th European Signal Processing Conference.

[15]  Kai Huang,et al.  Boosting accuracy of automated classification of fluorescence microscope images for location proteomics , 2004, BMC Bioinformatics.

[16]  C. Conrad,et al.  Automatic identification of subcellular phenotypes on human cell arrays. , 2004, Genome research.

[17]  Bernhard Schölkopf,et al.  Training Invariant Support Vector Machines , 2002, Machine Learning.

[18]  H. Ney,et al.  Enhancements for local feature based image classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[19]  Raphaël Marée,et al.  Random subwindows for robust image classification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Pierre Geurts,et al.  Segment and Combine Approach for Biological Sequence Classification , 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[21]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[22]  Raphaël Marée.  Classification automatique d'images par arbres de d'ecision , 2005 .

[23]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ambuj K. Singh,et al.  ViVo: visual vocabulary construction for mining biomedical images , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[25]  Pierre Geurts,et al.  Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..

[26]  Raphaël Marée,et al.  Biomedical Image Classification with Random Subwindows and Decision Trees , 2005, CVBIA.

[27]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[28]  Xiaobo Zhou,et al.  Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy , 2006, IEEE Transactions on Biomedical Engineering.

[29]  Cordelia Schmid,et al.  Toward Category-Level Object Recognition , 2006, Toward Category-Level Object Recognition.

[30]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Nathalie Harder,et al.  Feature Selection for Evaluating Fluorescence Microscopy Images in Genome-Wide Cell Screens , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[32]  Hermann Ney,et al.  The CLEF 2005 Automatic Medical Image Annotation Task , 2006, International Journal of Computer Vision.

[33]  R. Murphy,et al.  Automated subcellular location determination and high-throughput microscopy. , 2007, Developmental cell.

[34]  Pietro Perona,et al.  Automatic recognition of biological particles in microscopic images , 2007, Pattern Recognit. Lett..

[35]  Hanchuan Peng,et al.  Automatic recognition and annotation of gene expression patterns of fly embryos , 2007, Bioinform..

[36]  Systematic subcellular localization of novel proteins identified by large‐scale cDNA sequencing , 2009 .