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Institute of
                                   Information Systems




  Separating compound figures in journal
articles to allow for subfigure classification

                           Ajad Chhatkuli
            Antonio Foncubierta-Rodríguez
                        Dimitrios Markonis
                           Henning Müller
Motivation                                 Institute of
                                           Information Systems

•   Figures in biomedical journals contain a lot of
    information
•   CBIR has been proposed for accessing medical
    literature
•   Modality classification
    •   Improves accessibility
    •   Allows result filtering
    •   But 50% of figures are compound or multipanel
Aim                                               Institute of
                                                  Information Systems

•   Develop a system that separates compound figures
    in the biomedical literature
    •       Visual-information only
        •     Textual information is discarded
    •       Modality-independent
        •     One method for many images types
        •     Many methods for few images types
    •       Tunable according to the dataset
•   Large-scale tested
    •       Approximately 250 open access journals
Compound figure examples   Institute of
                           Information Systems
Methods. Dataset                       Institute of
                                       Information Systems

•   2982 manually classified figures from ImageCLEF
    2012 dataset
•   Ground truth:
    •   Image subclass: 2x1,1x2,
    •   Position of separators
Methods. Overview                                 Institute of
                                                  Information Systems

•   Problem is separated in two
    •       Find subfigure separator candidates
        •     Preprocessing if required
    •       Analyze candidates
        •     Remove false positives
        •     Rule-based decisions
Methods. Separator detection           Institute of
                                       Information Systems

 •       Based on minimum
         pixel projection for
         white-space separated
         figures
 •       Horizontal  Vertical
         detection
     •     Inverse order by rotation
           according to aspect ratio
     •     Recursive
Methods. Separator detection                       Institute of
                                                   Information Systems

 •       Rule-based processing
     •     Progressive truncation to remove labels if no
           separators are found
     •     Text removal based on connected commponents if no
           separators are found
     •     Complement image for black-space separations
     •     Standard deviation image for subtle separations
     •     Binarization of non-graph figures:
           •   Less than 40% of the image is white or almost white
Methods. Separator analysis                     Institute of
                                                Information Systems

 •       Classification problem
     •     True/false separator
 •       Features used:
     •     Closeness to border, division ratio, standard
           deviation, text removal analysis, histogram, gap
           comparison
 •       Classifiers:
     •     SVM
     •     Rule-based classifier
Results   Institute of
          Information Systems
Successful examples   Institute of
                      Information Systems
Successful examples   Institute of
                      Information Systems
Unsuccessful examples           Institute of
                                Information Systems

                    Not horizontal/vertical
No separation gap   separation
Conclusions future work                         Institute of
                                                Information Systems

 •       Good results for a wide range of images
 •       Using purely visual information
 •       Separation problem: detection and analysis
 •       Rule weights can be fine-tuned according to dataset
     •     What would be the impact of a larger training set?
     •     What would be the impact in existing modality
           classification accuracy?
Conclusions future work                         Institute of
                                                Information Systems

 •       Good results for a wide range of images
 •       Using purely visual information
 •       Separation problem: detection and analysis
 •       Rule weights can be fine-tuned according to dataset
     •     What would be the impact of a larger training set?
     •     What would be the impact in existing modality
           classification accuracy?
Institute of
                                                                     Information Systems




  Thanks for your attention!

                        More information at http://medgift.hevs.ch



Ajad Chhatkuli, Dimitrios Markonis, Antonio Foncubierta-Rodríguez, Fabrice Meriaudeau
and Henning Müller, Separating compound figures in journal articles to allow for subfigure
          classification, in: SPIE, Medical Imaging, Orlando, FL, USA, 2013

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Separating compound figures in journal articles to allow for subfigure classification

  • 1. Institute of Information Systems Separating compound figures in journal articles to allow for subfigure classification Ajad Chhatkuli Antonio Foncubierta-Rodríguez Dimitrios Markonis Henning Müller
  • 2. Motivation Institute of Information Systems • Figures in biomedical journals contain a lot of information • CBIR has been proposed for accessing medical literature • Modality classification • Improves accessibility • Allows result filtering • But 50% of figures are compound or multipanel
  • 3. Aim Institute of Information Systems • Develop a system that separates compound figures in the biomedical literature • Visual-information only • Textual information is discarded • Modality-independent • One method for many images types • Many methods for few images types • Tunable according to the dataset • Large-scale tested • Approximately 250 open access journals
  • 4. Compound figure examples Institute of Information Systems
  • 5. Methods. Dataset Institute of Information Systems • 2982 manually classified figures from ImageCLEF 2012 dataset • Ground truth: • Image subclass: 2x1,1x2, • Position of separators
  • 6. Methods. Overview Institute of Information Systems • Problem is separated in two • Find subfigure separator candidates • Preprocessing if required • Analyze candidates • Remove false positives • Rule-based decisions
  • 7. Methods. Separator detection Institute of Information Systems • Based on minimum pixel projection for white-space separated figures • Horizontal  Vertical detection • Inverse order by rotation according to aspect ratio • Recursive
  • 8. Methods. Separator detection Institute of Information Systems • Rule-based processing • Progressive truncation to remove labels if no separators are found • Text removal based on connected commponents if no separators are found • Complement image for black-space separations • Standard deviation image for subtle separations • Binarization of non-graph figures: • Less than 40% of the image is white or almost white
  • 9. Methods. Separator analysis Institute of Information Systems • Classification problem • True/false separator • Features used: • Closeness to border, division ratio, standard deviation, text removal analysis, histogram, gap comparison • Classifiers: • SVM • Rule-based classifier
  • 10. Results Institute of Information Systems
  • 11. Successful examples Institute of Information Systems
  • 12. Successful examples Institute of Information Systems
  • 13. Unsuccessful examples Institute of Information Systems Not horizontal/vertical No separation gap separation
  • 14. Conclusions future work Institute of Information Systems • Good results for a wide range of images • Using purely visual information • Separation problem: detection and analysis • Rule weights can be fine-tuned according to dataset • What would be the impact of a larger training set? • What would be the impact in existing modality classification accuracy?
  • 15. Conclusions future work Institute of Information Systems • Good results for a wide range of images • Using purely visual information • Separation problem: detection and analysis • Rule weights can be fine-tuned according to dataset • What would be the impact of a larger training set? • What would be the impact in existing modality classification accuracy?
  • 16. Institute of Information Systems Thanks for your attention! More information at http://medgift.hevs.ch Ajad Chhatkuli, Dimitrios Markonis, Antonio Foncubierta-Rodríguez, Fabrice Meriaudeau and Henning Müller, Separating compound figures in journal articles to allow for subfigure classification, in: SPIE, Medical Imaging, Orlando, FL, USA, 2013