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Biocybernetics and Biomedical Engineering

Tytuł artykułu

A new computer-based approach for fully automated segmentation of knee meniscus from magnetic resonance images

Autorzy Saygılı, A.  Albayrak, S. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Menisci are tissues that enable mobility and absorb excess loads on the knee. Problems in meniscus can trigger the disorder of osteoarthritis (OA). OA is one of the most common causes of disability, especially among young athlethes and elderly people. Therefore, the early diagnosis and treatment of abnormalities that occur in the meniscus are of significant importance. This study proposes a new computer-based and fully automated approach to support radiologists by: (i) the segmentation of medial menisci, (ii) enabling early diagnosis and treatment, and (iii) reducing the errors caused by MR intra-reader variability. In this study, 88 different MR images provided by the Osteoarthritis Initiative (OAI) are used. The histogram of oriented gradients (HOG) and local binary patterns (LBP) methods are used for feature extraction from these MR images along with the extreme learning machine (ELM) and random forests (RF) methods which are used for model learning (regression). As the first step of the pipeline, the most compact rectangular patches bounding the menisci are located. After this, meniscus boundaries are revealed by the morphological processes. Then, the similarities between these boundaries and the ground truth images are measured and compared with each other. The highest score is acquired with Dice similarity measurement with a success rate of 82%. A successful segmentation is performed on the diseased knee MR images. The proposed approach can be implemented as a decision support system for radiologists, while the segmented menisci can be used in classification of meniscal tear in future studies.
Słowa kluczowe
PL segmentacja automatyczna   staw kolanowy   operacja morfologiczna   obraz medyczny  
EN automated segmentation   knee joint   meniscus   morphological operations   medical image  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 432--442
Opis fizyczny Bibliogr. 44 poz., rys., tab., wykr.
autor Saygılı, A.
  • Department of Computer Engineering, Namık Kemal University, NKÜ Çorlu Mühendislik Fakültesi Silahtarağa Mahallesi Üniversite 1. Sokak, No: 13, 59860 Çorlu/Tekirdağ, Turkey,,
autor Albayrak, S.
[1] Esmaili Jah AA, Keyhani S, Zarei R, Moghaddam AK. Accuracy of MRI in comparison with clinical and arthroscopic findings in ligamentous and meniscal injuries of the knee. Acta Orthopaed Belg 2005;71:189–96.
[2] Zhengyi Y, Jurgen F, Shekhar SC, Aleš N, Ying X, Mark S, et al. Automatic bone segmentation and bone–cartilage interface extraction for the shoulder joint from magnetic resonance images. Phys Med Biol 2015;60:1441.
[3] Seim H, Kainmueller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S. Model-based auto-segmentation of knee bones and cartilage in MRI data. Medical Image Analysis for the Clinic: A Grand Challenge; 2010. pp. 215–23.
[4] Lorigo LM, Faugeras O, Grimson WEL, Keriven R, Kikinis R. Segmentation of bone in clinical knee MRI using texture-based geodesic active contours. In: Wells WM, Colchester A, Delp S, editors. Medical Image Computing and Computer-Assisted Intervention—MICCAI'98: First International Conference Cambridge, MA, USA, October 11–13. Berlin, Heidelberg: Springer; 1998. p. 1195–204.
[5] Schmid J, Magnenat-Thalmann N. MRI bone segmentation using deformable models and shape priors. In: Metaxas D, Axel L, Fichtinger G, Székely G, editors. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008: 11th International Conference, New York, NY, USA, September 6–10, Proceedings, Part I. Berlin, Heidelberg: Springer; 2008. p. 119–26.
[6] Dam EB, Folkesson J, Pettersen PC, Christiansen C. Semi- automatic knee cartilage segmentation. Proc SPIE Med; 2006.
[7] Cheong J, Suter D, Cicuttini F. Development of semi-automatic segmentation methods for measuring tibial cartilage volume. Digital Image Computing: Techniques and Applications (DICTA'05). 2005. p. 45.
[8] Öztürk CN, Albayrak S. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling. Comput Biol Med 2016;72:90–107.
[9] Shan L, Zach C, Charles C, Niethammer M. Automatic atlas-based three-label cartilage segmentation from MR knee images. Med Image Anal 2014;18:1233–46.
[10] Bui T, Ahn C, Lee Y-w, Shin J. Fully automatic segmentation based on localizing active contour method. Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication. Siem Reap, Cambodia: ACM; 2014. p. 1–5.
[11] Fripp J, Crozier S, Warfield SK, Ourselin S. Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging 2010;29:55–64.
[12] Folkesson J, Dam EB, Olsen OF, Pettersen PC, Christiansen C. Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans Med Imaging 2007;26:106–15.
[13] Sasaki T, Hata Y, Ando Y, Ishikawa M, Ishikawa H. Fuzzy rule-based approach to segment the menisci regions from MR images. Proc SPIE 3661, Medical Imaging. California: Spie Digital Library; 1999. p. 258–65.
[14] Hata Y, Kobashi S, Tokimoto Y, Ishikawa M, Ishikawa H. Computer aided diagnosis system of meniscal tears with T1 and T2 Weighted MR images based on fuzzy inference. In: Reusch B, editor. Computational Intelligence Theory and Applications: International Conference, 7th Fuzzy Days Dortmund, Germany, October 1–3, Proceedings. Berlin, Heidelberg: Springer; 2001. p. 55–8.
[15] Boniatis I, Panayiotakis G, Panagiotopoulos E. A computer-based system for the discrimination between normal and degenerated menisci from Magnetic Resonance Images. 2008 IEEE International Workshop on Imaging Systems and Techniques. 2008. pp. 335–9.
[16] Köse C, Gençalioğlu O, Şevik U. An automatic diagnosis method for the knee meniscus tears in MR images. Expert Systems Appl 2009;36:1208–16.
[17] Ramakrishna B, Liu W, Saiprasad G, Safdar N, Chang CI, Siddiqui K, et al. An automatic computer-aided detection system for meniscal tears on magnetic resonance images. IEEE Trans Med Imaging 2009;28:1308–16.
[18] Swanson MS, Prescott JW, Best TM, Powell K, Jackson RD, Haq F, et al. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees. Osteoarthritis Cartil 2010;18:344–53.
[19] Swamy MSM, Holi MS. Knee joint menisci visualization and detection of tears by image processing. 2012 International Conference on Computing Communication and Applications. 2012. pp. 1–5.
[20] Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Machine Intell 1986;PAMI-8:679–98.
[21] Patel AJ, Modi H, Patel H. Measurement of cartilage thickness in osteoarthritis and visualization of meniscus tear of knee MRI image processing. Int J Computer Sci Mob Comput 2016;5:39–52.
[22] Fripp J, Bourgeat P, Engstrom C, Ourselin S, Crozier S, Salvado O. Automated segmentation of the menisci from MR images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009. pp. 510–3.
[23] Nedmark F. Automatic segmentation of menisci in MR images using pattern recognition and graph cuts [Master]. Sweden: Lund Institute of Technology; 2011.
[24] Kim M-J, Yoo J-H, Hong H. Automatic segmentation of the meniscus based on active shape model in MR images through interpolated shape information. J KIISE: Comput Pract Lett 2010;16:1096–100.
[25] Yin Y, Anderson D, Williams R, Sonka M. Fully automated fast, and robust segmentation of the meniscus from MR images. ORS 2011 Annual Meeting, California; 2011.
[26] Paproki A, Engstrom C, Chandra SS, Neubert A, Fripp J, Crozier S. Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images data from the osteoarthritis initiative. Osteoarthritis Cartilage 2014;22:1259–70.
[27] Zhang K, Lu W, Marziliano P. The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images. Mach Vis Appl 2013;24:1459–72.
[28] Dam EB, Lillholm M, Marques J, Nielsen M. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative. J Med Imaging (Bellingham Wash) 2015;2:024001.
[29] Saygili A, Kaya H, Albayrak S. Automatic detection of meniscal area in the knee MR images. 24th Signal Processing and Communication Application Conference (SIU). 2016. pp. 1337–40.
[30] Us AK. Diz Menisküs Yaralanmaları (In Turkish); 2015, http://wwwartroskopius/dizmeniskusyaralanmahtm, Access Date: 22 August.
[31] ACL. Anatomy of the Knee. ACL Solution; 2016, http://wwwaclsolutionscom/anatomyphp, Access Date: 3 June.
[32] Tandoğan R. Menisküs yırtıklarında tedavi seçenekleri (In Turkish); 2015, http://wwwortoklinikcom/hastalar-icin/ meniskus-yirtiklari, Access Date: 26 August.
[33] Ordu S, Bayram E, Çetinus E, Kaya İ, Yılmaz M. Elli Yaş Altındaki Hastalarda Menisküs Yırtık Tiplerinin Ön Çapraz Bağ ve Osteokondral Lezyonlarla İlişkisi (In Turkish). Med Bull Haseki/Haseki Tip Bul 2014;52:177–80.
[34] Kellgren JH, Lawrence JS. Radiological assessment of osteo- arthrosis. Ann Rheumatic Dis 1957;16:494–502.
[35] Vedaldi A, Fulkerson B. Vlfeat: an open and portable library of computer vision algorithms. Proceedings of the 18th ACM International Conference on Multimedia. Firenze, Italy: ACM; 2010. p. 1469–72.
[36] Dalal N, Triggs B. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1. 2005. pp. 886–93.
[37] Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 2010;32:1627–45.
[38] Ojala T, Pietikäinen M, Mäenpää T. Gray scale and rotation invariant texture classification with local binary patterns. Computer Vision – ECCV 2000 6th European Conference on Computer Vision Dublin, Ireland, June 26 – July 1, 2000 Proceedings Part I. Berlin, Heidelberg: Springer; 2000. p. 404–20.
[39] Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing 2006;70:489–501.
[40] Guang-Bin H, Qin-Yu Z, Chee-Kheong S. Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Networks, 2004 Proceedings, IEEE International Joint Conference; 2004. p. 985–90.
[41] Breiman L. Random forests. Machine Learning 2001;45:5–32.
[42] Tin Kam H. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Machine Intell 1998;20:832–44.
[43] Tin Kam H. Random decision forests. Proceedings of the Third International Conference on Document Analysis and Recognition; 1995. p. 278–82.
[44] Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23–7.
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-8a526c92-0983-48d3-aae4-b53c8c15de55
DOI 10.1016/j.bbe.2017.04.008