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Segmenting Compound Biomedical Figures into Their Constituent Panels.

Pengyuan Li¹, Xiangying Jiang¹, Chandra Kambhamettu¹

¹Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA.

Experimental IR Meets Multilinguality, Multimodality, and Interaction : 8Th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings. Cross-Language Evaluation Forum. Conference

|January 15, 2019

Summary

This summary is machine-generated.

Accurate segmentation of biomedical figures is crucial for data extraction. This study introduces a novel method using Connected Component Analysis and quality assessment to improve compound figure segmentation, outperforming existing techniques.

Keywords:

Biomedical image Compound image separation Connected component analysis

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Area of Science:

Biomedical image analysis
Computational biology
Scientific data mining

Background:

Compound figures with multiple panels are common in biomedical publications.
Extracting visual information from these figures requires accurate panel segmentation.
Existing gap-detection methods often result in over- or under-segmentation errors.

Purpose of the Study:

To develop an improved method for segmenting compound figures in biomedical literature.
To address the limitations of current segmentation techniques, specifically over- and under-segmentation.
To enhance the accuracy and reliability of figure panel extraction for downstream analysis.

Main Methods:

Proposed a novel compound figure segmentation scheme utilizing Connected Component Analysis.
Developed a quality assessment step to evaluate and refine initial segmentations.
Implemented two re-segmentation methods to correct inaccurate initial segmentation results.

Main Results:

The proposed method demonstrated superior performance compared to other leading segmentation techniques.
The quality assessment and re-segmentation steps effectively addressed segmentation inaccuracies.
Experimental results validated the effectiveness and accuracy of the new approach.

Conclusions:

The developed Connected Component Analysis-based scheme offers a more robust solution for compound figure segmentation.
The integrated quality assessment and re-segmentation strategies significantly improve segmentation accuracy.
This approach facilitates more efficient and reliable harvesting of visual information from biomedical documents.