A novel image-based retrieval system for characterization of maxillofacial lesions in cone beam CT images
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The objective of medical content-based image retrieval (CBIR) is to assist clinicians in decision making by retrieving the most similar cases to a given query image from a large database. Herein, a new method for content-based image retrieval of cone beam CT (CBCT) scans is presented.
The introduced framework consists of two main phases: training database construction and querying. The goal of the training phase is database construction, which consists of three main steps. First, automatic segmentation of lesions using 3D symmetry analysis is performed. Embedding the prior shape knowledge of the 3D symmetry characteristics of the healthy human head structure increases the accuracy of automatic segmentation. Then, spatial pyramid matching is used for feature extraction, and the relative importance of each feature is learned using classifiers.
The method was applied to a dataset of 1145 volumetric CBCT images with four classes of maxillofacial lesions. A symmetry-based analysis model for automatic lesion segmentation was evaluated using similarity measures. Mean Dice coefficients of 0.89, 0.85, 0.92, and 0.87 were achieved for maxillary sinus perforation, radiolucent lesion, unerupted tooth, and root fracture classes, respectively. Moreover, the execution time of automatic segmentation was reduced to 3 min per case. The performance of the proposed search engine was evaluated using mean average precision and normalized discounted cumulative gain. A mean average retrieval accuracy and normalized discounted cumulative gain of 0.90 and 0.92, respectively, were achieved.
Quantitative results show that the proposed approach is more effective than previous methods in the literature, and it can facilitate the introduction of CBIR in clinical CBCT applications.
KeywordsCone beam CT Content-based medical image retrieval Maxillofacial lesions Spatial pyramid matching Symmetry analysis
This work is partly supported by MEXT Grant-in-Aid for Scientific Research No. 26108004. The authors would like to extend thanks to the clinical staffs of Taleghani Educational Hospital, Imam Hossein Educational Hospital, Guilan University of Medical Sciences, and Farzaneh Momeni Dental Imaging Center for clinical assistance and reviewing the cases.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
For this type of study, formal consent is not required because this study is a retrospective study.
Written informed consent was not required for this study because this study is a retrospective study.
- 2.Tyagi V (2017) Content-based image retrieval techniques: a review. In: Content-based image retrieval. Springer, pp 29–48Google Scholar
- 3.Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv Csur 40:5Google Scholar
- 4.Müller H, de Herrera AGS, Kalpathy-Cramer J, Demner-Fushman D, Antani SK, Eggel I (2012) Overview of the ImageCLEF 2012 medical image retrieval and classification tasks. In: CLEF online work. Noteslabsworkshop, pp 1–16Google Scholar
- 7.Long LR, Antani S, Lee D-J, Krainak DM, Thoma GR (2003) Biomedical information from a national collection of spine x-rays: film to content-based retrieval. In: Medical imaging 2003 PACS integrated medical information systems design evaluation. International Society for Optics and Photonics, pp 70–85Google Scholar
- 12.Langs G, Hanbury A, Menze B, Müller H (2012) VISCERAL: towards large data in medical imaging-challenges and directions. In: MICCAI international workshop on medical content-based retrieval for clinical decision support, pp 92–98Google Scholar
- 14.Bordes F, Berthier T, Di Jorio L, Vincent P, Bengio Y (2018) Iteratively unveiling new regions of interest in Deep Learning models. In: International conference on medical imaging with deep learning. Retrieved from https://openreview.net/pdf?id=rJz89iiiM. Accessed 13 Mar 2019
- 17.Loy G, Eklundh J-O (2006) Detecting symmetry and symmetric constellations of features. In: European conference on computer vision. Springer, pp 508–521Google Scholar
- 23.Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE conference on computer vision pattern recognition, CVPR 2009. IEEE, pp 1794–1801Google Scholar
- 24.Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, Prague. pp 1–2Google Scholar
- 25.Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. Technical report 7694, California Institute of Technology. http://www.vision.caltech.edu/Image_Datasets/Caltech256/
- 27.Atkinson KE (2008) An introduction to numerical analysis. Wiley, HobokenGoogle Scholar
- 29.Cignoni P, Rocchini C, Scopigno R (1998) Metro: measuring error on simplified surfaces. In: Computer graphics forum. Wiley, pp 167–174Google Scholar
- 31.Schütze H, Manning CD, Raghavan P (2008) Introduction to information retrieval. Cambridge University Press, CambridgeGoogle Scholar
- 34.Abdolali F, Zoroofi RA, Abdolali M (2015) Content based image retrieval for maxillofacial lesions. In: 9th Iranian conference on machine vision and image processing (MVIP). IEEE, pp 5–8Google Scholar