TY - GEN
T1 - Detecting figure-panel labels in medical journal articles using MRF
AU - You, Daekeun
AU - Antani, Sameer
AU - Demner-Fushman, Dina
AU - Govindaraju, Venu
AU - Thoma, George R.
PY - 2011
Y1 - 2011
N2 - We present a method for figure-panel (subfigure) label detection and recognition in multi-panel figures extracted from biomedical articles. Figures in biomedical articles often comprise several subfigures that are identified by superimposed panel labels ('A', 'B',...) which are referenced in the figure caption and discussion in the article body. Splitting such multi-panel figures into individual subfigures is a necessary step for improved multimodal biomedical information retrieval. Prior to feature extraction for indexing and retrieval of biomedical figures it is necessary to classify image content in each subfigure by its modality (X-ray, MRI, CT, etc.) and other relevant criteria. Subfigure labels are valuable in associating individual panels with relevant text in captions and discussion. We propose a 4-step panel label detection method based on Markov Random Field (MRF). Experiments on 515 multi-panel figures and analysis of the results show promising results. We present the successes and identify critical challenges.
AB - We present a method for figure-panel (subfigure) label detection and recognition in multi-panel figures extracted from biomedical articles. Figures in biomedical articles often comprise several subfigures that are identified by superimposed panel labels ('A', 'B',...) which are referenced in the figure caption and discussion in the article body. Splitting such multi-panel figures into individual subfigures is a necessary step for improved multimodal biomedical information retrieval. Prior to feature extraction for indexing and retrieval of biomedical figures it is necessary to classify image content in each subfigure by its modality (X-ray, MRI, CT, etc.) and other relevant criteria. Subfigure labels are valuable in associating individual panels with relevant text in captions and discussion. We propose a 4-step panel label detection method based on Markov Random Field (MRF). Experiments on 515 multi-panel figures and analysis of the results show promising results. We present the successes and identify critical challenges.
KW - belief propagation
KW - CBIR
KW - image binarization
KW - image classification
KW - image-text detection
KW - Markov Random Field
KW - Neural network
KW - OCR
UR - https://www.scopus.com/pages/publications/82355168364
U2 - 10.1109/ICDAR.2011.196
DO - 10.1109/ICDAR.2011.196
M3 - Conference contribution
AN - SCOPUS:82355168364
SN - 9780769545202
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 967
EP - 971
BT - Proceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
T2 - 11th International Conference on Document Analysis and Recognition, ICDAR 2011
Y2 - 18 September 2011 through 21 September 2011
ER -