Abstract
Multi-panel images are increasingly used in research and medical domains for describing complicated situations like results’ comparison in paper; or case depiction of a patient by combining all his medical images into a consolidated image. However, the content based image retrieval (CBIR) systems face the issue of performance decline in terms of poor retrieval accuracy because the individual sub-images of the multi-panel images cannot be accessed during the searching process. Representing multi-panel images in the form of sub-images is a necessary step for improving the retrieval accuracy of CBIR systems. The state-of-the-art multi-panel image segmentation approaches use recursive approach for sub-image separation, which detects the location of the sub-lines of a line in the multi-panel image appearing in its sub-images repeatedly. This characteristic of the available approaches makes the CBIR incapable to provide the intended results to the end users in real time. In this work, a line detection-based method using dynamic programming is proposed for sub-image separation, which detects the position of every line in the multi-panel image only once, instead of several times as in the case of state-of-art approaches. We evaluated the proposed method on a subset of the imageCLEFmed 2013 dataset, containing 1050 images belonging to different groups. The experimental results depict the effectiveness of the proposed method in term of generating the results quickly without losing the accuracy as compare to the state-of-the-art approaches.
Similar content being viewed by others
References
Ali M, Dong L, Akhtar R (2018) Multi-panel medical image segmentation framework for image retrieval system. Multimed Tools Appl 77(16):20271–20295
S Antani, D Demner-Fushman, J Li, BV Srinivasan, GR Thoma (2008). “Exploring use of images in clinical articles for decision support in evidence-based medicine”, proceeding of SPIE, document recognition and retrieval XV, 28
E Apostolova, D You, Z Xue, S Antani, D Demner-Fushman, and GR Thoma (2013). Image retrieval from scientific publications: text and image content processing to separate multipanel figures, J Am Soc Inf Sci Technol, pp. 893–908, 28
Neculai Archip, Rober Rohling, et al. (2005). “Ultrasound image segmentation using spectral clustering”, Volume 31, Issue 11, Pages 1485–1497, November
Aucar JA, Fernandez L, Wagner-Mann C (2007) If a picture is worth a thousand words, what is a trauma computerized tomography panel worth? Am J Surg 194:734–740
Xiaodong Bai, Zhiguo Cao, Zhenghong Yu, Hu Zhu (2011). “Color image segmentation using watershed and Nyström method based spectral clustering ”, seventh international symposium on multispectral image processing and pattern recognition (MIPPR2011), Guilin, China
CN Brown (1996). “100 years of x-rays”, Engineering Science and Education Journal, vol. 5, Issue 3
B Cheng, S Antani, RJ Stanley, D Demner-Fushman, and GR Thoma (2011). Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval, Proceedings of SPIE Electronic Imaging Science and Technology, Document Retrieval and Recognition XVIII
A Chhatkuli, A Foncubierta-Rodriguez, D Markonis, F Meriaudeau, H Muller (2013). “Separating compound figures in journal articles to allow for subfigure classification”, Proceeding of SPIE, Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications, vol. 8674
Cooper MS, Sommers-Herivel G, CT CTP, Mc-Carthy MB, Crawford BD, Phillips C (2004) The zebrafish dvd exchange project: a bioinformatics initiative. Methods Cell Biol 77:439–457
Kalpathy Cramer, H Muller et al (2011). The CLEF 2011 medical image retrieval and classification tasks. Working Notes of CLEF 2011 (Cross Language Evaluation Forum), September
Satoshi Tsutsui David J. Crandall (2017). A data driven approach for compound figure separation using convolutional neural networks, computer vision and pattern recognition
García Seco de Herrera Alba and Kalpathy Cramer Jayashree and Demner Fushman Dina and Antani Sameer and Müller, Henning (2013). “Overview of the Image-CLEF 2013 medical tasks”, Working Notes of CLEF 2013 (Cross Language Evaluation Forum)
Alba García Seco de Herrera, Jayashree Kalpathy Cramer, Dina Demner Fushman, Sameer Antani and Henning Müller (2013). Overview of the ImageCLEF 2013 medical tasks, in: CLEF working notes 2013, Valencia
AGS de Herrera, J Kalpathy Cramer et al (2013). Overview of the imageCLEF 2013 medical tasks. Working notes of CLEF 2013, Spain
Demner-Fushman D, Antani S, Simpson M, Thoma GR (2009) Annotation and retrieval of clinically relevant images. Int J Med Inform 78:e59–e67
D Demner-Fushman, S Antani, and GR Thoma (2007). Automatically finding images for clinical decision support [C]. Seventh IEEE International Conference on Data Mining - Workshops
Doulamis AD, Doulamis ND (2004) Optimal content-based video decomposition for interactive video navigation. IEEE Transactions on Circuits and Systems for Video Technology 14(6):757–775
Doulamis ND, Kokkinos P, Varvarigos E (2012) Resource selection for tasks with time requirements using spectral clustering. IEEE Trans Comput 63(2):461–474
SP Gou, X Zhuang, and LC Jiao (2012). Quantum immune fast spectral clustering for SAR image segmentation, IEEE Geoscience and Remote Sensing Letters, volume: 9 , Issue: 1
W Hersh, H Müller, J Kalpathy-Cramer, E Kim, X Zhou (2008). The consolidated imageCLEFmed medical image retrieval task test collection [J]. J Digit Imaging
N Jhanwar, S Chaudhuri, G Seetharaman, B Zavidovique (2004). Content based image retrieval using motif co occurrence matrix [J]. Image Vision Computing
J Kalpathy-Cramer, W Hersh, S Bedrick, H Muller (2008). Query analysis to improve medical image retrieval[C]. Society for Imaging Informatics in Medicine (SIIM) 2008, Seattle, USA, May
J Kalpathy-Cramera, W Hersh (2007). Automatic image modality based classification and annotation to improve medical image retrieval [J]. Studies in Health Technology and Informatics, 1334–1338
Li P, Jiang X et al (2018) Compound image segmentation of published biomedical figures. Bioinformatics 34(7):1192–1199
LD Lopez, J Yu, CO Tudor, CN Arighi, H Huang, K Vijay-Shanker, CH Wu (2012). “Robust segmentation of biomedical figures for image-based document retrieval”, IEEE international conference on Bioinformatics and Biomedicine (BIBM), October, 1–6
Luxburg V (2007) Ulrike. A tutorial on spectral clustering. Statistics and computing April 17:395–416
Ma W-Y, H Zhang (1999). Content-based image indexing and retrieval. Handbook of Multimedia Computing, CRC Press, Palo Alto
H Muller, Kalpathy Cramer et al (2008). Overview of the imageCLEFmed 2008 medical image retrieval task. Evaluating Systems for Multilingual and Multimodal Information Access, Denmark 2009, 512–522
H Muller, Kalpathy Cramer et al (2010). Overview of the clef 2009 medical image retrieval track [C]. Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments. CLEF’09, Berlin, Heidelberg
Muller H et al (2010) ImageCLEF experimental evaluation in visual information retrieval. The Springer International Series on Information Retrieval. Springer, Berlin Heidelberg, pp 277–294
H Muller, et al (2012). Overview of the imageCLEF 2012 medical image retrieval and classification tasks. Working Notes of CLEF 2012 (Cross Language Evaluation Forum), September
Ng Andrew Y, Michael I Jordan, and Yair Weiss (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems
N Otsu (1979). A threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems, Man, and [Cybernetics
Rui Y, Huang TS (1999) Image retrieval: Current techniques, promising directions and open issues [J]. J Vis Commun Image Represent 10:39–62
Rui Y, Huang S et al (2002) Relevance feedback: a power tool for interactive content-based image retrieval [J]. IEEE Transactions on Circuits and Video Technology, August, pp 644–655
Shin KG, Zheng Q (1991) Scheduling job operations in an automatic assembly line. IEEE T Robotic Autom 7(3):333–341
S Sreedevi, S Sebastian (2012). Content based image retrieval based on database revision[C]. IEEE International Conference on Machine Vision and Image Processing (MVIP), 29–32
Tagare HD, Jaffe C, Duncan J (1997) Medical image databases: A content-based retrieval approach [J]. J Am Med Inform Assoc 4:184–198
Mario Taschwer and Oge Marques (2016). Compound Figure Separation Combining Edge and Band Separator Detection, International conference on multimedia modeling, pp 162–173
Zhang Y, PB Luh, K Yoneda, T Kano and Y Kyoya (1997). Mixed-model assembly line scheduling using the lagrangian relaxation technique. Proceedings of the IEEE International Conference on Control Applications. Hartford, CT, USA, pp: 429–434
Zheng, Xin, et al (2004). Locality preserving clustering for image database. Proceedings of the 12th annual ACM international conference on Multimedia
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ali, M., Asghar, M.Z. & Baloch, A. An efficient approach for sub-image separation from large-scale multi-panel images using dynamic programming. Multimed Tools Appl 80, 5449–5471 (2021). https://doi.org/10.1007/s11042-020-09950-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09950-y