Abstract
The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter, we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg–Marquardt neural networks. The process requires that we first run several experiments to determine the individual features that offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach, we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein subcellular localization databases.
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References
Boland MV & Murphy RF (2001). A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics, 17:1213–1223.
Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy RF & Kovačević J (2007). A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 8:210.
Chen X & Murphy RF (2005). Objective clustering of proteins based on subcellular location patterns. Journal of Biomedicine Biotechnology, 2:87–95.
Chen S-C, Zhao T, Gordon GJ & Murphy RF (2007). Automated image analysis of protein localization in budding yeast. Bioinformatics, 23:66–71.
Conrad C, Erfle H, Warnat P, Daigle N, Lorch T, Ellenberg J, Pepperkok R & Eils R (2004). Automatic identification of subcellular phenotypes on human cell arrays. Genome Research, 14(6):1130–1136.
Eisenhaber F & Bork P (1998). Wanted: subcellular localization of proteins based on sequence. Trends in Cell Biology, 8:169–170.
Fink JL, Aturaliya RN, Davis MJ, Zhang F, Hanson K, Teasdale MS & Teasdale RD (2006). LOCATE: a protein subcellular localization database. Nucleic Acids Research, 34(database issue):D213–D217.
Glory E, Newberg J & Murphy RF (2008). Automated comparison of protein subcellular location patterns between images of normal and cancerous tissues. In ISBI, 304–307.
Hagan MT & Menhaj M (1999). Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6):989–993.
Hamilton N, Pantelic R, Hanson K & Teasdale RD (2007). Fast automated cell phenotype classification. BMC Bioinformatics, 8:110.
Haralick RM (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):768–804.
Ho TK (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832–844.
Huang K & Murphy RF (2004). Automated classification of subcellular patterns in multicell images without segmentation into single cells. In IEEE International Symposium on Biomedical Imaging: Nano to Macro, Arlington, VA, USA, 1139–1142.
Huang K & Murphy R (2004). Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics, 5:78, doi:10.1186/1471-2105-5-78.
Lin CC, Tsai Y-S, Lin Y-S, Chiu T-Y, Hsiung C-C, Lee M-I, Simpson JC & Hsu C-N (2007). Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization. Bioinformatics, 23(24):3374–3381.
Nakai K & Horton P (1999). Psort: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Science, 24:34–35.
Nanni L & Lumini A (2008). A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 43(2):87–97.
Nanni L & Lumini A (2009). Ensemble of neural networks for automated cell phenotype image classification. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques.
Ojala T, Pietikainen M & Maeenpaa T (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987.
Ojansivu V & Heikkilä J (2008). Blur insensitive texture classification using local phase quantization. In Proc. 3rd International Conference on Image and Signal Processing (ICISP 2008), volume 5099 of LNCS, Springer, Berlin, 236–243.
Tan X, Triggs B (2007). Enhanced local texture feature sets for face recognition under difficult lighting conditions. In Analysis and Modelling of Faces and Gestures, volume 4778 of LNCS, Springer, Heidelberg, 168–182.
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Nanni, L., Brahnam, S., Lumini, A. (2010). Novel Features for Automated Cell Phenotype Image Classification. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_24
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_24
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