Abstract
One of the routine tasks in the Orthopedics practice is the articular cartilage assessment. Proper cartilage assessment includes a precise localization, and recognition of spots indicating the cartilage loss caused by the osteoarthritis. Unfortunately, such tasks are performed manually, without the SW feedback, which leads to various clinical outputs based on the physician’s experience. Based on such facts, a development of the fully automatic systems bringing automatic modeling and classification of the cartilage is clinically very important. In our paper we have proposed a local thresholding multiregional segmentation method for the cartilage segmentation from the MR (Magnetic Resonance) images. In our approach, an optimal configuration of the fuzzy triangular sets is driven by the FCM clustering to obtain an optimal segmentation model based on the thresholding. We have verified the proposed model on a sample of the 200 MR image records containing the early osteoarthritis signs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kubicek, J., Penhaker, M., Augustynek, M., Cerny, M., Oczka, D.: Multiregional soft segmentation driven by modified ABC algorithm and completed by spatial aggregation: volumetric, spatial modelling and features extraction of articular cartilage early loss. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 385–394. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_37
Kubicek, J., Vicianova, V., Penhaker, M., Augustynek, M.: Time deformable segmentation model based on the active contour driven by Gaussian energy distribution: extraction and modeling of early articular cartilage pathological interuptions. Front. Artif. Intell. Appl. 297, 242–255 (2017)
Kubicek, J., Valosek, J., Penhaker, M., Bryjova, I.: Extraction of chondromalacia knee cartilage using multi slice thresholding method. In: Vinh, P.C., Alagar, V. (eds.) ICCASA 2015. LNICST, vol. 165, pp. 395–403. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29236-6_37
Kubicek, J., Penhaker, M., Bryjova, I., Kodaj, M.: Articular cartilage defect detection based on image segmentation with colour mapping. In: Hwang, D., Jung, Jason J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 214–222. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11289-3_22
Kim, J.J., Nam, J., Jang, I.G.: Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm. Comput. Methods Programs Biomed. 154, 161–171 (2018)
Pitikakis, M., et al.: Automatic measurement and visualization of focal femoral cartilage thickness in stress-based regions of interest using three-dimensional knee models. Int. J. Comput. Assist. Radiol. Surg. 11(5), 721–732 (2016)
Kumarv, A., Jayanthy, A.K.: Classification of MRI images in 2D coronal view and measurement of articular cartilage thickness for early detection of knee osteoarthritis. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings, art. no. 7808167, pp. 1907–1911 (2017)
Mallikarjuna Swamy, M.S., Holi, M.S.: Knee joint cartilage visualization and quantification in normal and osteoarthritis. In: International Conference on Systems in Medicine and Biology, ICSMB 2010 - Proceedings, art. no. 5735360, pp. 138–142 (2010)
Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans. Med. Imaging 29(1), 55–64 (2010). art. no. 5071225
Wang, P., He, X., Lyu, Y., Li, Y.-M., Qiu, M.-G., Liu, S.-J.: Automatic segmentation of articular cartilages using multi-feature SVM and elastic region growing. Jilin Daxue Xuebao (Gongxueban)/J. Jilin Univ. (Eng. Technol. Ed.) 46(5), 1688–1688 (2016)
Gougoutas, A.J., et al.: Cartilage volume quantification via Live Wire segmentation. Acad. Radiol. 11(12), 1389–1395 (2004)
Dodin, P., Pelletier, J.P., Martel-Pelletier, J., Abram, F.: Automatic human knee cartilage segmentation from 3D magnetic resonance images. IEEE Trans. Bio-Med. Eng. 57(11), 2699–2711 (2010)
Xia, Y., Manjon, J.V., Engstrom, C., Crozier, S., Salvado, O., Fripp, J.: Automated cartilage segmentation from 3D MR images of hip joint using an ensemble of neural networks. In: Proceedings - International Symposium on Biomedical Imaging, art. no. 7950701, pp. 1070–1073 (2017)
Acknowledgment
The work and the contributions were supported by the project SV4508811/2101 Biomedical Engineering Systems XIV’. This study was also supported by the research project The Czech Science Foundation (GACR) 2017 No. 17-03037S Investment evaluation of medical device development run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. This study was supported by the research project The Czech Science Foundation (TACR) ETA No. TL01000302 Medical Devices development as an effective investment for public and private entities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kubicek, J., Krestanova, A., Penhaker, M., Augustynek, M., Cerny, M., Oczka, D. (2019). Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-14802-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14801-0
Online ISBN: 978-3-030-14802-7
eBook Packages: Computer ScienceComputer Science (R0)