NLM at imageCLEF2015: Biomedical Multipanel Figure Separation

This paper summarizes the participation of the National Li- brary of Medicine (NLM) in the imageCLEF 2015 biomedical multipanel gure separation task. In this task, our method uses two dierent tech- niques that are employed on the basis of characteristics of the gures: 1) stitched multipanel gure separation; and 2) multipanel gure sepa- ration with homogeneous gaps. Fusion of the two techniques achieved an accuracy of 84.64%.

[1]  Hayit Greenspan,et al.  Content-Based Image Retrieval in Radiology: Current Status and Future Directions , 2010, Journal of Digital Imaging.

[2]  Henning Müller,et al.  Overview of the ImageCLEF 2012 Medical Image Retrieval and Classification Tasks , 2012, CLEF.

[3]  Jie Yao,et al.  Searching online journals for fluorescence microscope images depicting protein subcellular location patterns , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[4]  Henning Müller.  Medical (Visual) Information Retrieval , 2012, PROMISE Winter School.

[5]  José Francisco Aldana Montes,et al.  General Overview of ImageCLEF at the CLEF 2015 Labs , 2015, CLEF.

[6]  Manabu Torii,et al.  A framework for biomedical figure segmentation towards image-based document retrieval , 2013, BMC Systems Biology.

[7]  Henning Müller,et al.  Separating compound figures in journal articles to allow for subfigure classification , 2013, Medical Imaging.

[8]  R. Joe Stanley,et al.  Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval , 2011, Electronic Imaging.

[9]  Henning Müller,et al.  Overview of the ImageCLEF 2015 Medical Classification Task , 2015, CLEF.

[10]  Henning Müller,et al.  Overview of the ImageCLEF 2013 Medical Tasks , 2013, CLEF.

[11]  George R. Thoma,et al.  Design and Development of a Multimodal Biomedical Information Retrieval System , 2012, J. Comput. Sci. Eng..

[12]  Daekeun You,et al.  Image retrieval from scientific publications: Text and image content processing to separate multipanel figures , 2013, J. Assoc. Inf. Sci. Technol..

[13]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[14]  J. C. R. Licklider.  A picture is worth a thousand words: and it costs... , 1969, AFIPS '69 (Spring).

[15]  Hong Yu,et al.  Towards Answering Biological Questions with Experimental Evidence: Automatically Identifying Text that Summarize Image Content in Full-Text Articles , 2006, AMIA.

[16]  Carey Phillips,et al.  The Zebrafish DVD Exchange Project: a bioinformatics initiative. , 2004, Methods in cell biology.

[17]  Rafael Grompone von Gioi,et al.  LSD: a Line Segment Detector , 2012, Image Process. Line.

[18]  Daekeun You,et al.  Interactive cross and multimodal biomedical image retrieval based on automatic region-of-interest (ROI) identification and classification , 2014, International Journal of Multimedia Information Retrieval.

[19]  George R. Thoma,et al.  Stitched Multipanel Biomedical Figure Separation , 2015, 2015 IEEE 28th International Symposium on Computer-Based Medical Systems.

[20]  C. Wagner-Mann,et al.  If a picture is worth a thousand words, what is a trauma computerized tomography panel worth? , 2007, American journal of surgery.

[21]  George R. Thoma,et al.  Multimodal biomedical image indexing and retrieval using descriptive text and global feature mapping , 2013, Information Retrieval.

[22]  Henning Müller,et al.  Overview of the CLEF 2011 Medical Image Classification and Retrieval Tasks , 2011, CLEF.

[23]  Dragutin Petkovic,et al.  Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review , 1996 .