Figure text extraction in biomedical literature

PLoS ONE

Volumn 6, Issue 1, 2011, Pages

  Kim, Daehyun ^a   Yu, Hong ^a  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; AUTOMATION; DATA EXTRACTION; IMAGE PROCESSING; IMAGE QUALITY; INFORMATION RETRIEVAL; MEDICAL LITERATURE; MEDLINE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; COMPUTER GRAPHICS; COMPUTER PROGRAM; DATA MINING; INTERNET; METHODOLOGY; PUBLICATION; STANDARD;

COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; DATA MINING; INFORMATION STORAGE AND RETRIEVAL; INTERNET; PATTERN RECOGNITION, AUTOMATED; PERIODICALS AS TOPIC; PUBMED; SOFTWARE;

EID: 79251579651     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0015338     Document Type: Article

References (52)

1. Shatkay H, Chen N, Blostein D (2006) Integrating image data into biomedical text categorization. Bioinformatics 22(14): 446-453.
2. Rafkind B, Lee M, Chang SF, Yu H (2006) Exploring text and image features to classify images in bioscience literature. Proc. of the BioNLP workshop on Linking Natural Language Processing and Biology. pp 73-80.
3. Yu H, Lee M (2006) Accessing bioscience images from abstract sentences. Bioinformatics 22(14): 547-556.
4. Hearst MA, Divoli A, Guturu H, Ksikes A, Nakov P, et al. (2007) BioText Search Engine: beyond abstract search. Bioinformatics 23(16): 2196-2197.
5. Kahn CE, Thao C (2007) GoldMiner: a radiology image search engine. American Journal of Roentgenology 188: 1475-1478.
6. Qian Y, Murphy RF (2008) Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical models. Bioinformatics 23(4): 569-576.
7. Xu S, McCusker J, Krauthammer M (2008) Yale Image Finder (YIF): a new search engine for retrieving biomedical images. Bioinformatics 24(17): 1968-1970.
8. Ahmed A, Xing EP, Cohen WW, Murphy RF (2009) Structured correspondence topic models for mining captioned figures in biological literature. International Conference on Knowledge Discovery and Data Mining. pp 39-47.
9. Murphy RF, Velliste M, Yao J, Porreca G (2001) Searching online journals for fluorescence microscope images depicting protein subcellular location patterns. IEEE International Symposium on Bio-Informatics and Biomedical Engineering (BIBE). pp 119-128.
10. Yu H, Lee M (2006) BioEx: a novel user-interface that accesses images from abstract sentences. New York, USA: HLT-NAACL.
11. Agarwal S, Yu H (2009) FigSum: automatically generating structured text summaries for figures in biomedical literature. AMIA Annual Symposium.
12. Yu H, Agarwal S, Johnston M, Cohen A (2009) Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension. Journal of Biomedical Discovery and Collaboration 4.
13. Yu H, Liu F, Ramesh BP (2010) Automatic figure ranking and user interfacing for intelligent biomedical figure search. PLoS ONE (in press).
14. Shatkay H, Pan F, Rzhetsky A, Wilbur WJ (2008) Multi-dimensional classification of biomedical text: toward automated, practical provision of high-utility text to diverse users. Bioinformatics 24: 2086-2093.
15. Kim D, Yu H (2009) Hierarchical image classification in the bioscience literature. AMIA Annual Symposium.
16. Agarwal S, Yu H (2009) Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. Bioinformatics 25(23): 3174-3180.
17. Liu Z, Sarkar S (2008) Robust outdoor text detection using text intensity and shape features. International Conference on Pattern Recognition.
18. Chen D, Odobez J, Thiran J (2004) A localization/verification scheme for finding text in images and videos based on contrast independent features and machine learning methods. Image Communication 19(3): 205-217.
19. Gatos B, Pratikakis I, Perantonis SJ (2005) Text detection in indoor/outdoor scene images. International Workshop on Camera-based Document Analysis and Recognition. pp 127-132.
20. Chen D, Odobez J, Bourlard H (2004) Text detection and recognition in images video frames. Pattern Recognition 37: 595-608.
21. Anthimopoulos M, Gatos B, Pratikakis I (2008) A hybrid system for text detection in video frames. IAPR Workshop on Document Analysis System. Pp 286-292.
22. Kim DH, Sohn K (2008) Static text region detection in video sequences using color and orientation consistencies. International Conference on Pattern Recognition.
23. Shivakumara P, Huang W, Tan CL (2008) Efficient video text detection using edge features. International Conference on Pattern Recognition
24. Kou Z, Cohen WW, Murphy RF (2003) Extracting information from text and images for location proteomics. ACM SIGKDD Workshop on Data Mining in Bioinformatics.
25. Gonzalez R, Woods R (2002) Digital image processing. Upper Saddle River, NJ: Prentice Hall, 2nd edition. pp 75-146.
26. Hsieh H, Andrew H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans. on Acoustics Speech Signal Process 26(6): 508-517.
27. http://www.ScanStore.com/Software/.
28. Chen X, Yang J, Zhang J, Waibel A (2004) Automatic detection and recognition of signs from natural scenes. IEEE Transactions on Image Processing 13: 87-99.
29. Chen X, Yuille AL (2004) Detecting and reading text in natural scenes. International Conference on Computer Vision and Pattern Recognition (CVPR). pp 366-373.
30. Weinman JJ, Learned-Miller E, Hanson A (2007) Fast lexicon-based scene text recognition with sparse belief propagation. International Conference on Document Analysis and Recognition
31. Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3): 171-176.
32. Wagner RA, Michael JF (1974) The string-to-string correction problem. J ACM 21(1): 168-173.
33. Riseman EM, Hanson AR (1974) A contextual postprocessing system for error correction using binary n-grams. IEEE Trans Comput. 23(5): 480-493.
34. Zamora EM (1981) The use of trigram analysis for spelling error detection. Information Processing and Management 17(6): 305-16.
35. Kashyap RL, Oommen J (1984) Spelling correction using probabilistic methods. Pattern Recognition Letters 2: 147-154.
36. Hodge V, Jim A (2001) A novel binary spell checker. In Artificial Neural Networks. ICANN. pp 1199-1204.
37. Lucas SM, Patoulas G, Downton AC (2003) Fast lexicon-based word recognition in noisy index card images. International Conference on Document Analysis and Recognition.
38. Schambach MP (2005) Fast script word recognition with very large vocabulary. International Conference on Document Analysis and Recognition.
39. Ruch P (2002) Using contextual spelling correction to improve retrieval effectiveness in degraded text collections. In Proceedings of the 19th international conference on Computational linguistics 1: 1-7.
40. Li M, Yang Z, Muhua Z, Ming Z (2006) Exploring distributional similarity based models for query spelling correction. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. pp 1025-1032.
41. Ringlstetter C, K US, Stoyan M (2007) Adaptive text correction with Webcrawled domain-dependent dictionaries. ACM Trans. Speech Lang Process 4(4): 9.
42. Donoser M, Bischof H, Wagner S (2009) Using web search engines to improve text recognition. International Conference on Pattern Recognition
43. Tong X, Evans DA (1996) A statistical approach to automatic OCR error correction in context. Proc. of the 4th Workshop on Very Large Corpora.
44. Thillou C, Ferreira S, Gosselin B (2005) An embedded application for degraded text recognition. EURASIP Journal on Applied Signal Processing 13: 2127-2135.
45. Stehouwer H, Zaanen M (2009) Language models for contextual error detection and correction. Proc. of the EACL Workshop on Computational Linguistic Aspects of Grammatical Inference. pp 41-48.
46. Jones MP, James HM (1997) Contextual spelling correction using latent semantic analysis. In Proceedings of the 5th conference on applied natural language processing. pp 166-173.
47. Martins B, Mário JS (2004) Spelling correction for search engine queries. In Advances in Natural Language Processing. pp 372-383.
48. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10: 707-10.
49. Paterson M, Dancik V (1994) Longest common subsequences. International Symposium on Mathematical Foundations of Computer Science 841: 127-142.
50. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22: 4673-4680.
51. Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. International Conference on Computer Vision (ICCV2009)
52. Fattal R (2007) Image upsampling via imposed edge statistics. ACM Trans. Graphics (Proc. SIGGRAPH 2007) 26(3): 95-102.