ABSTRACT
The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The experimental results demonstrate that the proposed method has an average accuracy rate of 96.10% with an average MCC rate of 88.26%, which significantly outperforms existing intensity-based methods such as fuzzy c-means clustering and deep feature extraction method.
- E. J. Park, M. S. Cho, S. J. Baek, H. Hur, B. S. Min, S. H. Baik, K. Y. Lee, and N. K. Kim. 2016. Long-term oncologic outcomes of robotic low anterior resection for rectal cancer: a comparative study with laparoscopic surgery. Post-publication peer review of the biomedical literature.Google Scholar
- C.B. Chng, Y. Ho, and C.K. Chui. 2015. Automation of retinal surgery: A shared control robotic system for laser ablation. 2015 IEEE International Conference on Information and Automation.Google Scholar
- J. Fripp, S. Crozier, S. K. Warfield, and S. Ourselin. 2007. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee. Physics in Medicine and Biology, vol. 52, no. 6, pp. 1617--1631.Google ScholarCross Ref
- J. Schmid and N. Magnenat-Thalmann. 2008. MRI Bone Segmentation Using Deformable Models and Shape Priors. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 Lecture Notes in Computer Science, pp. 119--126. Google ScholarDigital Library
- A. R. Lanfranco, A. E. Castellanos, J. P. Desai, and W. C. Meyers. 2004. Robotic Surgery A Current Perspective, Lippincott Williams & Wilkins.Google Scholar
- A. J. Teichtahi, A. E. Wluka, M. L. Dabies, and F. M. Cicuttini. 2008. Imaging of knee osteoarthritis. Best Pratice & Researcj Clinical Rheumatology, vol. 22, no. 6, pp. 10651--1074.Google Scholar
- B. Pirzamanbin, A fully automated segmentation of knee bones and cartilage using shape context and active shape models. Lund: Lund University, 2012.Google Scholar
- T. G. Williams, A. P. Holmes, J. C. Waterton, R. A. Maciewicz, C. E. Hutchinson, R. J. Moots, A. F. P. Nash, and C. J. Taylor. 2010. Anatomically Corresponded Regional Analysis of Cartilage in Asymptomatic and Osteoarthritic Knees by Statistical Shape Modelling of the Bone. IEEE Transactions on Medical Imaging, vol. 29, no. 8, pp. 1541--1559.Google ScholarCross Ref
- P. Bourgeat, J. Fripp, P. Stanwell, S. Ramadan, and S. Ourselin. 2007. MR image segmentation of the knee bone using phase information. Medical Image Analysis, vol. 11, no. 4, pp. 325--335.Google ScholarCross Ref
- A. Aprovitola and L. Gallo. 2016. Knee bone segmentation from MRI: A classification and literature review. Biocybernetics and Biomedical Engineering, vol. 36, no. 2, pp. 437--449.Google ScholarCross Ref
- R. Dalvi, R. Abugharbieh, D. Wilson, and D. R. Wilson. 2007. Multi-Contrast MR for Enhanced Bone Imaging and Segmentation. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Google Scholar
- J.S. Lee and Y.N. Chung. 2005. Integrating Edge Detection And Thresholding Approaches To Segmenting Femora And Patellae From Magnetic Resonance Images. Biomedical Engineering: Applications, Basis and Communications, vol. 17, no. 01, pp. 1--11.Google ScholarCross Ref
- J. Y. Mun, J. Y. Lee, D. Y. Kim, and S. Shin. 2017. Extract texture-problematic femur from Knee MRI using Fuzzy C-means and Region Growing approach. International Conference on Internet.Google Scholar
- Y. Sun, E. C. Teo, and Q. H. Zhang. 2007. Discussions of Knee joint segmentation. Biomedical and Pharmaceutical Engineering, 2006. ICBPE 2006. International Conference on. IEEE 2006.Google Scholar
- J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, 2013Google Scholar
- S. Shen, W. Sandham, M. Granat, and A. Sterr. 2005. MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization. IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp. 459--467. Google ScholarDigital Library
- S. Chen and D. Zhang. 2004. Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 34, no. 4, pp. 1907--1916. Google ScholarDigital Library
- L. Szilagyi, Z. Benyo, S. Szilagyi, and H. Adam. MR brain image segmentation using an enhanced fuzzy C-means algorithm. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).Google Scholar
- F. Ambellan, A. Tack, M. Ehlke, and S. Zachow. April, 2018. Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative. Venues. Available: https://openreview.net/forum?id=SJ_-Nx3jz.Google Scholar
- C. Kirbas and F. Quek, 2004. A review of vessel extraction techniques and algorithms. ACM Computing Surveys(CSUR), 36, 2 (Jun. 2004), 81--121. Google ScholarDigital Library
- S. Udomhunsakul and P. Wongsita. 2004. Feature extraction in medical MRI images. IEEE Conference on Cybernetics and Intelligent Systems.Google Scholar
- K. D. Kharat, V. J. Pawar, and S. R. Pardeshi. 2016. Feature extraction and selection from MRI images for the brain tumor classification. International Conference on Communication and Electronics Systems (ICCES).Google Scholar
- E. B. George and M. Karnan. 2012. MRI Brain Image Enhancement Using Filtering Techniques. International Journal of Computer Science & Engineering Technology (IJCSET). Vol. 3 No. 9, 399--403.Google Scholar
- J. Y. Lee, J. Y. Mun, M. Taheri, S. H. Son, and S. Shin. 2017. Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI. Proceedings of the International Conference on Research in Adaptive and Convergent Systems - RACS 17. Google ScholarDigital Library
- "MRI Data," MRI Data. {Online}. Available: http://mridata.org/fullysampledGoogle Scholar
- A. Krizhevsky, I. Sutskever, G. E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, vol. 60, no. 6, 84--90. Google ScholarDigital Library
Index Terms
- A SSLBP-based feature extraction framework to detect bones from knee MRI scans
Recommendations
E-fuzzy feature fusion and thresholding for morphology segmentation of brain MRI modalities
AbstractBrain tumor segmentation is a significant procedure in medical image processing. Effective and efficient segmentation is always a key concern for the radiologists due to the presence of low illumination in imaging modalities of Magnetic Resonance (...
Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI
RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent SystemsBlood vessel segmentation has been developed in the liver, heart, and retinal images due to accurate description and analysis of vascular structure plays a crucial role in clinical routine. Since the varicose vein, deep vein thrombosis, and occlusive ...
MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique
Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from ...
Comments