Novel features for automated cell phenotype image classification

Advances in Experimental Medicine and Biology

Volumn 680, Issue , 2010, Pages 207-213

  Nanni, Loris ^b   Brahnam, Sheryl ^a   Lumini, Alessandra ^b  

^a MISSOURI STATE UNIVERSITY   (United States)

^b NONE

Author keywords

Image processing in medicine and biological sciences; Pattern classification and recognition

Indexed keywords

ACCURACY; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CELLULAR DISTRIBUTION; CLASSIFICATION ALGORITHM; CONFERENCE PAPER; DATA BASE; INTERMETHOD COMPARISON; PHENOTYPE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN LOCALIZATION; QUALITY CONTROL; ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; CELL; FACTUAL DATABASE; HELA CELL; HUMAN; IMAGE PROCESSING; METABOLISM; MOUSE;

PROTEIN;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CELLS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; HELA CELLS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PHENOTYPE; PROTEINS;

EID: 79952202651     PISSN: 00652598     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4419-5913-3_24     Document Type: Conference Paper

References (21)

1. 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. (Pubitemid 33735348)
2. Chebira A, Barbotin Y, Jackson C, Merryman T, Srinivasa G, Murphy RF & Kovacevic J (2007). A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 8:210.
3. Chen X & Murphy RF (2005). Objective clustering of proteins based on subcellular location patterns. Journal of Biomedicine Biotechnology, 2:87-95. (Pubitemid 41627864)
4. Chen S-C, Zhao T, Gordon GJ & Murphy RF (2007). Automated image analysis of protein localization in budding yeast. Bioinformatics, 23:66-71.
5. 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. (Pubitemid 38811549)
6. Eisenhaber F & Bork P (1998). Wanted: subcellular localization of proteins based on sequence. Trends in Cell Biology, 8:169-170. (Pubitemid 28142265)
7. 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.
8. 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.
9. Hagan MT & Menhaj M (1999). Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6):989-993.
10. Hamilton N, Pantelic R, Hanson K & Teasdale RD (2007). Fast automated cell phenotype classification. BMC Bioinformatics, 8:110.
11. Haralick RM (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):768-804.
12. Ho TK (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844. (Pubitemid 128741345)
13. 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. (Pubitemid 40514143)
14. 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.
15. 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. (Pubitemid 350238891)
16. 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. (Pubitemid 29074461)
17. Nanni L & Lumini A (2008). A reliable method for cell phenotype image classification. Artificial Intelligence in Medicine, 43(2):87-97.
18. 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.
19. 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. (Pubitemid 34835471)
20. Ojansivu V & Heikkila 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.
21. 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.