Knee Meniscus Segmentation and Tear Detection from MRI: A Review

Author(s): Ahmet Saygili*, Songül Albayrak.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 1 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Automatic diagnostic systems in medical imaging provide useful information to support radiologists and other relevant experts. The systems that help radiologists in their analysis and diagnosis appear to be increasing.

Discussion: Knee joints are intensively studied structures, as well. In this review, studies that automatically segment meniscal structures from the knee joint MR images and detect tears have been investigated. Some of the studies in the literature merely perform meniscus segmentation, while others include classification procedures that detect both meniscus segmentation and anomalies on menisci. The studies performed on the meniscus were categorized according to the methods they used. The methods used and the results obtained from such studies were analyzed along with their drawbacks, and the aspects to be developed were also emphasized.

Conclusion: The work that has been done in this area can effectively support the decisions that will be made by radiology and orthopedics specialists. Furthermore, these operations, which were performed manually on MR images, can be performed in a shorter time with the help of computeraided systems, which enables early diagnosis and treatment.

Keywords: Knee joint, CAD, segmentation, meniscus, tear detection, MRI, medical image.

[1]
Kawahara T, Sasho T, Katsuragi J, Ohnishi T, Haneishi H. Relationship between knee osteoarthritis and meniscal shape in observation of Japanese patients by using magnetic resonance imaging. J Orthop Surg Res 2017; 12(1): 97.
[http://dx.doi.org/10.1186/s13018-017-0595-y] [PMID: 28651649]
[2]
Zhang B, Zhang Y, Cheng H, et al. Computer-aided knee joint magnetic resonance image segmentation-a survey. arXiv preprint arXiv: 180204894 2018.
[3]
Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans Med Imaging 2008; 27(5): 629-40.
[http://dx.doi.org/10.1109/TMI.2007.912817] [PMID: 18450536]
[4]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[5]
Khotanlou H, Colliot O, Atif J, Bloch I. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 2009; 160(10): 1457-73.
[http://dx.doi.org/10.1016/j.fss.2008.11.016]
[6]
Menze BH, van Leemput K, Lashkari D, Weber M-A, Ayache N, Golland P. A generative model for brain tumor segmentation in multi-modal images. In: Jiang T, Navab N, Pluim JPW, Viergever MA, Eds Medical Image Computing and Computer-Assisted Intervention 13th International Conference. Springer; Beijing, China; September 20-24 2010; pp. 151-9.
[http://dx.doi.org/10.1007/978-3-642-15745-5_19]
[7]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[8]
Prastawa M, Bullitt E, Ho S, Gerig G. A brain tumor segmentation framework based on outlier detection. Med Image Anal 2004; 8(3): 275-83.
[http://dx.doi.org/10.1016/j.media.2004.06.007] [PMID: 15450222]
[9]
Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G. Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol 2003; 10(12): 1341-8.
[http://dx.doi.org/10.1016/S1076-6332(03)00506-3] [PMID: 14697002]
[10]
Sehgal A, Goel S, Mangipudi P, Mehra A, Tyagi D, Eds. Automatic brain tumor segmentation and extraction in MR images.Conference on Advances in Signal Processing (CASP). IEEE: Pune, India 2016; pp. 104-7.
[http://dx.doi.org/10.1109/CASP.2016.7746146]
[11]
Bernardi D, Macaskill P, Pellegrini M, et al. Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study. Lancet Oncol 2016; 17(8): 1105-13.
[http://dx.doi.org/10.1016/S1470-2045(16)30101-2] [PMID: 27345635]
[12]
Gur D, Sumkin JH, Rockette HE, et al. Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst 2004; 96(3): 185-90.
[http://dx.doi.org/10.1093/jnci/djh067] [PMID: 14759985]
[13]
Houssami N, Bernardi D, Pellegrini M, et al. Breast cancer detection using single-reading of breast tomosynthesis (3D-mammography) compared to double-reading of 2D-mammography: Evidence from a population-based trial. Cancer Epidemiol 2017; 47: 94-9.
[http://dx.doi.org/10.1016/j.canep.2017.01.008] [PMID: 28192742]
[14]
Krammer J, Pinker-Domenig K, Robson ME, et al. Breast cancer detection and tumor characteristics in BRCA1 and BRCA2 mutation carriers. Breast Cancer Res Treat 2017; 163(3): 565-71.
[http://dx.doi.org/10.1007/s10549-017-4198-4] [PMID: 28343309]
[15]
Kriege M, Brekelmans CT, Boetes C, et al. Magnetic Resonance Imaging Screening Study Group. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med 2004; 351(5): 427-37.
[http://dx.doi.org/10.1056/NEJMoa031759] [PMID: 15282350]
[16]
Tabár L, Fagerberg CJ, Gad A, et al. Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. Lancet 1985; 1(8433): 829-32.
[http://dx.doi.org/10.1016/S0140-6736(85)92204-4] [PMID: 2858707]
[17]
Kim C, Yoon J, Lee Y-J. Medical image segmentation by more sensitive adaptive thresholding.International Conference on IT Convergence and Security (ICITCS) IEEE. Prague, Czech Republic; 2016; pp. 1-3.
[18]
Li Y, Bai X, Jiao L, Xue Y. Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput 2017; 56: 345-56.
[http://dx.doi.org/10.1016/j.asoc.2017.03.018]
[19]
Lim YW, Lee SU. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognit 1990; 23(9): 935-52.
[http://dx.doi.org/10.1016/0031-3203(90)90103-R]
[20]
Polakowski WE, Cournoyer DA, Rogers SK, et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging 1997; 16(6): 811-9.
[http://dx.doi.org/10.1109/42.650877] [PMID: 9533581]
[21]
Yadav AK, Roy R, Somwanshi D. Thresholding and morphological based segmentation techniques for medical images. In: International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE; Jaipur, India 2016; pp. 1-5.
[http://dx.doi.org/10.1109/ICRAIE.2016.7939573]
[22]
Duan H-H, Gong J, Nie S-D. Two-pass region growing combined morphology algorithm for segmenting airway tree from CT chest scans.UKACC 11th International Conference on Control. IEEE; Belfast, UK 2016; pp. 1-6.
[http://dx.doi.org/10.1109/CONTROL.2016.7737635]
[23]
Hojjatoleslami SA, Kittler J. Region growing: a new approach. IEEE Trans Image Process 1998; 7(7): 1079-84.
[http://dx.doi.org/10.1109/83.701170] [PMID: 18276325]
[24]
Pan Z, Lu J. A Bayes-based region-growing algorithm for medical image segmentation. Comput Sci Eng 2007; 9(4): 32-8.
[http://dx.doi.org/10.1109/MCSE.2007.67]
[25]
Pohle R, Toennies KD, Eds. Segmentation of medical images using adaptive region growing Proceedings of Medical Imaging 2001: Image Processing. SPIE: San Diego, USA 2001.
[http://dx.doi.org/10.1117/12.431013]
[26]
Pourghassem H, Ghassemian H. Content-based medical image classification using a new hierarchical merging scheme. Comput Med Imag Grap 2008; 32(8): 651-61.
[http://dx.doi.org/10.1016/j.compmedimag.2008.07.006] [PMID: 18789648]
[27]
Tallapragada VS, Reddy DM, Kiran PS, Reddy DV. A novel medical image segmentation and classification using combined feature set and decision tree classifier. IJET 2016; 4(9): 83-6.
[28]
Wells WM, Grimson WL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imaging 1996; 15(4): 429-42.
[http://dx.doi.org/10.1109/42.511747] [PMID: 18215925]
[29]
Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen T-J. Fuzzy C-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 2006; 30(1): 9-15.
[30]
Ng H, Ong S, Foong K, Goh P, Nowinski W. Medical image segmentation using K-means clustering and improved watershed algorithm. In: IEEE Southwest Symposium on Image Analysis and Interpretation. IEEE; Denver; USA 2006; pp. 61-5.
[http://dx.doi.org/10.1109/SSIAI.2006.1633722]
[31]
Tuan TM. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 2016; 46: 380-93.
[http://dx.doi.org/10.1016/j.eswa.2015.11.001]
[32]
Dong X, Shen J, Shao L, Van Gool L. Sub-Markov random walk for image segmentation. IEEE Trans Image Process 2016; 25(2): 516-27.
[http://dx.doi.org/10.1109/TIP.2015.2505184] [PMID: 26661298]
[33]
Held K, Rota Kops E, Krause BJ, Wells WM III, Kikinis R, Müller-Gärtner H-W. Markov random field segmentation of brain MR images. IEEE Trans Med Imaging 1997; 16(6): 878-86.
[http://dx.doi.org/10.1109/42.650883] [PMID: 9533587]
[34]
Li H-D, Kallergi M, Clarke LP, Jain VK, Clark RA. Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 1995; 14(3): 565-76.
[http://dx.doi.org/10.1109/42.414622] [PMID: 18215861]
[35]
Ward PG, Ferris NJ, Raniga P, et al. Vein segmentation using shape-based Markov Random Fields.Biomedical Imaging (ISBI 2017) IEEE 14th International Symposium on Biomedical Imaging. IEEE; Melbourne, VIC, Australia; 2017; pp. 1133-6.
[http://dx.doi.org/10.1109/ISBI.2017.7950716]
[36]
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001; 20(1): 45-57.
[http://dx.doi.org/10.1109/42.906424] [PMID: 11293691]
[37]
Jiang J, Trundle P, Ren J. Medical image analysis with artificial neural networks. Comput Med Imaging Graph 2010; 34(8): 617-31.
[http://dx.doi.org/10.1016/j.compmedimag.2010.07.003] [PMID: 20713305]
[38]
Reddick WE, Glass JO, Cook EN, Elkin TD, Deaton RJ. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans Med Imaging 1997; 16(6): 911-8.
[http://dx.doi.org/10.1109/42.650887] [PMID: 9533591]
[39]
Roth HR, Lu L, Liu J, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 2016; 35(5): 1170-81.
[http://dx.doi.org/10.1109/TMI.2015.2482920] [PMID: 26441412]
[40]
Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 2016; 30: 108-19.
[http://dx.doi.org/10.1016/j.media.2016.01.005] [PMID: 26917105]
[41]
Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 2009; 13(4): 543-63.
[http://dx.doi.org/10.1016/j.media.2009.05.004] [PMID: 19525140]
[42]
McInerney T, Terzopoulos D. Deformable models in medical image analysis: a survey. Med Image Anal 1996; 1(2): 91-108.
[http://dx.doi.org/10.1016/S1361-8415(96)80007-7] [PMID: 9873923]
[43]
Phellan R, Falcão AX, Udupa JK. Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models. Med Phys 2016; 43(1): 401-10.
[http://dx.doi.org/10.1118/1.4938577] [PMID: 26745933]
[44]
Weese J, Kaus M, Lorenz C, Lobregt S, Truyen R, Pekar V, Eds. Shape constrained deformable models for 3D medical image segmentation. Biennial International Conference on Information Processing in Medical Imaging. Springer; Berlin, Heidelberg 2001; pp. 380-7.
[http://dx.doi.org/10.1007/3-540-45729-1_38]
[45]
Akselrod-Ballin A, Galun M, Gomori MJ, Basri R, Brandt A. Atlas guided identification of brain structures by combining 3D segmentation and SVM classification.Med Image Comput Comput Assist Interv. 2006; 9: pp. (Pt 2)209-16.
[46]
Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solórzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging 2009; 28(8): 1266-77.
[http://dx.doi.org/10.1109/TMI.2009.2014372] [PMID: 19228554]
[47]
Karasawa K, Oda M, Kitasaka T, et al. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure. Med Image Anal 2017; 39: 18-28.
[http://dx.doi.org/10.1016/j.media.2017.03.006] [PMID: 28410505]
[48]
Birkfellner W. Applied medical image processing: a basic course. CRC Press 2015.
[49]
Zhang H, Gao Z, Xu L, et al. A meshfree representation for cardiac medical image computing. IEEE J Transl Eng Health Med 2018; 6: 1-12.
[http://dx.doi.org/10.1109/JTEHM.2018.2795022] [PMID: 29531867]
[50]
Zhen X, Zhang H, Islam A, Bhaduri M, Chan I, Li S. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression. Med Image Anal 2017; 36: 184-96.
[http://dx.doi.org/10.1016/j.media.2016.11.008] [PMID: 27940226]
[51]
He X, Zhang H, Landis M, Sharma M, Warrington J, Li S. Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation. Med Image Anal 2017; 36: 22-40.
[http://dx.doi.org/10.1016/j.media.2016.10.009] [PMID: 27816860]
[52]
Schmidler C. Knee joint anatomy, function and problems Available from: https://www.healthpages.org/anatomy-function/knee-joint-structure-function-problems/
[53]
[54]
Philip T. Knee MRI sequences Available from: http://www.freitas-rad.net/pages/Basic_MSK_MRI/Knee.htm
[55]
Physicool. Anatomy of the knee joint 2018. Available from: https://www.physicool.co.uk/guide/sprained-knee/
[56]
Seim H, Kainmueller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S. Model-based auto-segmentation of knee bones and cartilage in MRI data. In: Medical Image Analysis for the Clinic. Beijing: A Grand Challenge 2010.
[57]
Yang Z, Fripp J, Chandra SS, et al. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images. Phys Med Biol 2015; 60(4): 1441-59.
[http://dx.doi.org/10.1088/0031-9155/60/4/1441] [PMID: 25611124]
[58]
Gandhamal A, Talbar S, Gajre S, Razak R, Hani AFM, Kumar D. Fully automated subchondral bone segmentation from knee MR images: data from the Osteoarthritis Initiative. Comput Biol Med 2017; 88: 110-25.
[http://dx.doi.org/10.1016/j.compbiomed.2017.07.008] [PMID: 28711767]
[59]
Lorigo L, Faugeras O, Grimson W, Keriven R, Kikinis R. Segmentation of bone in clinical knee MRI using texture-based geodesic active contours. MICCAI 1998; 1998: 1195-204.
[http://dx.doi.org/10.1007/BFb0056309]
[60]
Schmid J, Magnenat-Thalmann N. MRI bone segmentation using deformable models and shape priors.International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; Shenzhen, China; 2019; pp. 119-26.
[http://dx.doi.org/10.1007/978-3-540-85988-8_15]
[61]
Cheong J, Suter D, Cicuttini F. Development of semi-automatic segmentation methods for measuring tibial cartilage volume Proceedings on Digital Image Computing: Techniques and Applications. IEEE: Cairns, Australia 2005.
[62]
Dama EB, Folkessona J, Pettersenb PC, Christiansenb C. Semi-automatic knee cartilage segmentation.Proc SPIE Int Soc Opt Eng. 2006; p. 6144.
[63]
Bui T, Ahn C, Lee Y-w, Shin J, Eds. Fully automatic segmentation based on localizing active contour method.Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication. ACM, Inc.; NY, USA 2014.
[http://dx.doi.org/10.1145/2557977.2558036]
[64]
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(1): 106-15.
[http://dx.doi.org/10.1109/TMI.2006.886808] [PMID: 17243589]
[65]
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(1): 55-64.
[http://dx.doi.org/10.1109/TMI.2009.2024743] [PMID: 19520633]
[66]
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.
[http://dx.doi.org/10.1016/j.compbiomed.2016.03.011] [PMID: 27017069]
[67]
Shan L, Zach C, Charles C, Niethammer M. Automatic atlas-based three-label cartilage segmentation from MR knee images. Med Image Anal 2014; 18(7): 1233-46.
[http://dx.doi.org/10.1016/j.media.2014.05.008] [PMID: 25128683]
[68]
Xia Y, Manjón JV, Engstrom C, Crozier S, Salvado O, Fripp J. Automated cartilage segmentation from 3D MR images of hip joint using an ensemble of neural networks Biomedical Imaging (ISBI 2017). IEEE 14th International Symposium on Biomedical Imaging. IEEE; Melbourne, VIC, Australia 2017; pp. 1070-3.
[69]
Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med 2018; 79(4): 2379-91.
[PMID: 28733975]
[70]
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; Shenzhen, China; 2013.
[http://dx.doi.org/ 10.1007/978-3-642-40763-5_31]
[71]
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) 2015; 2(2)024001
[http://dx.doi.org/10.1117/1.JMI.2.2.024001] [PMID: 26158096]
[72]
Fripp J, Bourgeat P, Engstrom C, Ourselin S, Crozier S, Salvado O. Automated segmentation of the menisci from MR images.International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; Boston, USA 2009; pp. 510-13.
[http://dx.doi.org/10.1109/ISBI.2009.5193096]
[73]
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(9): 1259-70.
[http://dx.doi.org/10.1016/j.joca.2014.06.029] [PMID: 25014660]
[74]
Swanson MS, Prescott JW, Best TM, et al. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees. Osteoarthritis Cartilage 2010; 18(3): 344-53.
[http://dx.doi.org/10.1016/j.joca.2009.10.004] [PMID: 19857510]
[75]
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(7): 1459-72.
[http://dx.doi.org/10.1007/s00138-012-0466-9]
[76]
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 2010; 16(11): 1096-100.
[77]
Aldrin F. Automated segmentation of the meniscus KTH, School of Engineering Sciences (SCI) Master’s Thesi 2017; 69.
[78]
Xu C, Pham DL, Prince JL. Image segmentation using deformable models. In: Handbook of medical. Imaging 2000; 2: 129-74.
[http://dx.doi.org/10.1117/3.831079.ch3]
[79]
Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2(1): 315-37.
[http://dx.doi.org/10.1146/annurev.bioeng.2.1.315] [PMID: 11701515]
[80]
Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis 1988; 1(4): 321-31.
[http://dx.doi.org/10.1007/BF00133570]
[81]
Terzopoulos D. Regularization of inverse visual problems involving discontinuities. IEEE Trans Pattern Anal Mach Intell 1986; (4): 413-24.
[http://dx.doi.org/10.1109/TPAMI.1986.4767807]
[82]
Cootes TF, Edwards GJ, Taylor CJ. Comparing active shape models with active appearance models. UK: BMVC 1999.
[83]
Köse C, Gençalioğlu O, Şevik U. An automatic diagnosis method for the knee meniscus tears in MR images. Expert Syst Appl 2009; 36: 1208-16.
[http://dx.doi.org/10.1016/j.eswa.2007.11.036]
[84]
Fu J-C, Lin C-C, Wang C-N, Ou Y-K. Computer-aided diagnosis for knee meniscus tears in magnetic resonance imaging. JIPE 2013; 30(2): 67-77.
[http://dx.doi.org/10.1080/10170669.2012.761285]
[85]
Paproki A, Engstrom C, Strudwick M, et al. Automated T2-mapping of the menisci from magnetic resonance images in patients with acute knee injury. Acad Radiol 2017; 24(10): 1295-304.
[http://dx.doi.org/10.1016/j.acra.2017.03.025] [PMID: 28551397]
[86]
Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994; 16(6): 641-7.
[http://dx.doi.org/10.1109/34.295913]
[87]
Boniatis I, Panayiotakis G, Panagiotopoulos E. A computer-based system for the discrimination between normal and degenerated menisci from magnetic resonance images.International Workshop on Imaging Systems and Techniques. IEEE; Crete, Greece 2008; pp. 335-9.
[http://dx.doi.org/10.1109/IST.2008.4659996]
[88]
Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging 1994; 13(4): 716-24.
[http://dx.doi.org/10.1109/42.363096] [PMID: 18218550]
[89]
Grau V, Mewes AU, Alcañiz M, Kikinis R, Warfield SK. Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 2004; 23(4): 447-58.
[http://dx.doi.org/10.1109/TMI.2004.824224] [PMID: 15084070]
[90]
Meyer F, Beucher S. Morphological segmentation. J Vis Commun Image Represent 1990; 1(1): 21-46.
[http://dx.doi.org/10.1016/1047-3203(90)90014-M]
[91]
Adalsteinsson D, Sethian JA. A fast level set method for propagating interfaces. J Comput Phys 1995; 118(2): 269-77.
[http://dx.doi.org/10.1006/jcph.1995.1098]
[92]
Kohut P, Holak K, Obuchowicz R. Image processing in detection of knee joints injuries based on MRI images. J Vibroeng 2017; 19(5): 3822-31.
[http://dx.doi.org/10.21595/jve.2017.17931]
[93]
Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognit 1993; 26(9): 1277-94.
[http://dx.doi.org/10.1016/0031-3203(93)90135-J]
[94]
Ramakrishna B, Liu W, Saiprasad G, et al. An automatic computer-aided detection system for meniscal tears on magnetic resonance images. IEEE Trans Med Imaging 2009; 28(8): 1308-16.
[http://dx.doi.org/10.1109/TMI.2009.2014864] [PMID: 19237341]
[95]
Dudhmande RP, Rajurkar AM, Kottawar VG. Extraction of whole and torn meniscus in M.R.I. images and detection of meniscal tears.1st International Conference on Intelligent Systems and Information Management (ICISIM). IEEE; Aurangabad, India; 2017; pp. 11-7.
[96]
Swamy MM, Holi M. Knee joint menisci visualization and detection of tears by image processing.International Conference on Computing, Communication and Applications. IEEE; Dindigul, Inida 2012; pp. 1-5.
[http://dx.doi.org/10.1109/ICCCA.2012.6179203]
[97]
Patel J, Modi H, Patel H. Measurement of cartilage thickness in osteoarthritis and visualization of meniscus tear of knee mri image processing. IJCSMC 2016; 5(1): 39-52.
[98]
Yin Y, Anderson D, Williams R, Sonka M. Fully automated, fast, and robust segmentation of the meniscus from MR images.ORS 2011 Annual Meeting. Long Beach, CA, USA 2011.
[99]
Saygili A, Kaya H, Albayrak S, Eds. Automatic detection of meniscal area in the knee MR images. Signal Processing and Communication Application Conference (SIU). Zonguldak, Turkey: IEEE 2016; pp. 1337-40.
[http://dx.doi.org/10.1109/SIU.2016.7495995]
[100]
Rohlfing T, Brandt R, Menzel R, Maurer CR Jr. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 2004; 21(4): 1428-42.
[http://dx.doi.org/10.1016/j.neuroimage.2003.11.010] [PMID: 15050568]
[101]
Rohlfing T, Brandt R, Menzel R, Russakoff D, Maurer J, Calvin R. The handbook of medical image analysis-volume III: registration models. In: Kluwer Academic. Plenum Publishers 2005; pp. 435-86.
[102]
Kalinić H. Atlas-based image segmentation: a survey. Universiy of Zagreb 2009; 2009: 1-7.
[103]
Dam EB. Simple methods for scanner drift normalization validated for automatic segmentation of knee magnetic resonance imaging-with data from the osteoarthritis initiative 2017. arXiv preprint arXiv:171208425
[104]
Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 2012; 29(6): 82-97.
[http://dx.doi.org/10.1109/MSP.2012.2205597]
[105]
Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 1997; 8(1): 98-113.
[http://dx.doi.org/10.1109/72.554195] [PMID: 18255614]
[106]
MIT. MIT technology review Available from: https://www.technologyreview.com/
[107]
Dice LR. Measures of the amount of ecologic association between species. Ecology 1945; 26(3): 297-302.
[http://dx.doi.org/10.2307/1932409]
[108]
Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 2018; 8(1): 1727.
[http://dx.doi.org/10.1038/s41598-018-20132-7] [PMID: 29379060]
[109]
Tack A, Mukhopadhyay A, Zachow S. Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative. Osteoarthritis Cartilage 2018; 26(5): 680-8.
[http://dx.doi.org/10.1016/j.joca.2018.02.907] [PMID: 29526784]
[110]
Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosci 1984; 10(2-3): 191-203.
[http://dx.doi.org/10.1016/0098-3004(84)90020-7]
[111]
Sasaki T, Hataa Y, Andob Y, Ishikawa M, Ishikawac H. Fuzzy rule based approach to segment the menisci region from MR images Proceedings of Medical Imaging: Image Processing. SPIE: San Diego, CA, USA 1999.
[112]
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 computational intelligence theory and applications.International Conference on Computational Intelligence. pringer; Berlin, Heidelberg 2001; pp. 55-8.
[113]
Zarandi MH, Khadangi A, Karimi F, Turksen IB. A computer-aided type-ii fuzzy image processing for diagnosis of meniscus tear. J Digit Imaging 2016; 29(6): 677-95.
[http://dx.doi.org/10.1007/s10278-016-9884-y] [PMID: 27198133]
[114]
Saygili A, Albayrak S, Eds. Meniscus segmentation and tear detection in the knee MR images by fuzzy C-means method.Signal Processing and Communications Applications Conference (SIU). IEEE; Antalya, Turkey 2017; pp. 1-4.
[http://dx.doi.org/10.1109/SIU.2017.7960183]
[115]
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. Hata Y, Kobashi S, Tokimoto Y, Ishikawa M, Ishikawa H. Lect Notes Comput Sci 2001; 2001: pp. 55-8.
[http://dx.doi.org/10.1007/3-540-45493-4_9]
[116]
Saygılı A, Albayrak S. A new computer-based approach for fully automated segmentation of knee meniscus from magnetic resonance images. Biocybern Biomed Eng 2017; 37(3): 432-42.
[http://dx.doi.org/10.1016/j.bbe.2017.04.008]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 16
ISSUE: 1
Year: 2020
Page: [2 - 15]
Pages: 14
DOI: 10.2174/1573405614666181017122109
Price: $65

Article Metrics

PDF: 24
HTML: 4
PRC: 1