Online Biological Literature MRI Image Classification Study

Abstract:

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

• Periodical: Advanced Materials Research (Volumes 694-697)
• Pages: 2336-2340
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.694-697.2336
• Online since: May 2013
• Authors: Yun Feng Yang*, Feng Xian Tang
• Keywords: Feature Fusion, Image Classification, MRI, PHOW

References

[1] A. M. Cohen, W. R. Hersh. The TREC 2004 genomics trackcategorization task: classifying full text biomedical documents[C]. Proc. of the thirteenth Text Retrieval Conference,2006: 1-4.

[2] C. Blaschke, E. A. Leon, M. Krallinger, and A:BMC Bioinformatics. 2005,6(16): 1471-2105.

[3] Y.Qian, R.F. Murphy: Bioinformatics, 2008, 24 (4): 569-576.

[4] R.F. Murphy, M.Velliste, J.Yao , et al. Searching online journals for fluorescence microscope images depicting protein subcellular location patterns[C]. IEEE International Symposium on Bioinformatics and Bioengineering, 2001: 119-128.

DOI: 10.1109/bibe.2001.974420

[5] R.F. Murphy, M.Velliste,G.Porreca: The Journal of VLSI Signal Processing, 2003, 35 (3): 311-321.

[6] A.Koike , T.Takagi . Classifying biomedical figures using combination of bag of keypoints and bag of words[C]. International Conference on Complex, Intelligent and Software Intensive Systems, 2009: 848-853

DOI: 10.1109/cisis.2009.11

[7] N.Ishii , A. Koike , Y.Yamamoto , et al:Artificial Intelligence in Medicine, 2010, 49 (3): 135-143

[8] S.Lazebnik, C.Schmid, J.Ponce . Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C]. Proceedings of the IEEE Computer Society Conference of Computer Vision and Pattern Recognition (CVPR'06), New York, USA, 2006. 2: 2169-2178.

DOI: 10.1109/cvpr.2006.68

[9] ZHAO Pengfei, QIAN Yuntao, ZHENG Wenbin. Detecting And Recognizing Character Located In Online Literature's Figures[A]. The 8th joint conference of national signal and information processing[C]. China stereology society, Beijing, 2009: 151-155..

[10] ZHAO Pengfei, Q IAN Yuntao, ZHENG Wenbin:CHINESE JOURNAL OF STEREOLOGY AND IMAGE ANALYS, 2009.14(2):216-221

[11] ZHENG Zhuo zhao , SUN Zhong qiang, FAN Jia dong , et al:China Medical Imaging Technology, 2002,18(6): 587-588.

[12] Fawcett T: Pattern Recognition Letters, 2006, 27(8): 816-874.