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Abstrakty
A multi-step methodology resulting in a three-dimensional visualization and construction of feature vector of posterior cruciate ligament is presented. In the first step the location of the posterior cruciate ligament is established using the fuzzy image concept. The fuzzy image concept is based on the entropy measure of fuzziness extended to two dimensions. In order to reduce the area of analysis, the region of interest including the ligament structures is detected. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges was implemented. After finding the region of interest, the fuzzy connectedness procedure was performed. This procedure permitted to extract the ligament structures. On the basis of the extracted posterior cruciate ligament structures, the three-dimensional visualization of this ligament was built and, with the support of experts' knowledge, an appropriate feature vector was constructed and its values assigned for normal and pathological cases. Correct results were obtained for over 88% of 97 cases.
Wydawca
Czasopismo
Rocznik
Tom
Strony
657--669
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
autor
- Silesian University of Technology, Faculty of Biomedical Engineering, Zabrze, Poland
Bibliografia
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- [3] Kisielewski Y, Ciszek B. The development of phylogenetic and ontogenetic and role of the cruciate ligament in the knee joint. Acta Clin 2001;4(1):275–7.
- [4] Ciszkowska-Lyson B. The anatomy of the cruciate ligament in the MRI study. Acta Clin 2001;4(1):321–30.
- [5] Zarychta P. Features extraction in anterior and posterior cruciate ligaments analysis. Comput Med Imaging Graph 2015;46:108–20. http://dx.doi.org/10.1016/j.compmedimag.2015.03.001.
- [6] Zarychta P. Cruciate ligaments of the knee joint in the computer analysis. In: Pietka E, Kawa J, Wieclawek W, editors. Information technologies in biomedicine advances in intelligent and soft computing, vol. 283. Heidelberg: Springer-Verlag Berlin; 2014. p. 71–80.
- [7] Davarinos N, O'Neill B, Curtin W. A brief history of anterior cruciate ligament reconstruction. Adv Orthop Surg 2014. http://dx.doi.org/10.1155/2014/706042. Article ID 706042, 6 pp.
- [8] Alcala-Galiano A, Baeva M, Ismael M, Argueso MJ. Imaging of posterior cruciate ligament (PCL) reconstruction: normal postsurgical appearance and complications. Skelet Radiol 2014;43(12):1659–68.
- [9] Hensler D, Van Eck C, Fu FH, Irrgang J. Anatomic anterior cruciate ligament reconstruction utilizing the double-bundle technique. J Orthop Sports Phys Ther 2012;42(3): 184–95.
- [10] Wright RW, Gill CS, Chen L, Brophy RH, Matava MJ, Smith MV, et al. Outcome of revision anterior cruciate ligament reconstruction: a systematic review. J Bone Joint Surg 2012;94(6):531–6.
- [11] Kruse LM, Gray B, Wright LW. Rehabilitation after anterior cruciate ligament reconstruction. J Bone Joint Surg Am 2012;94(19):1737–48.
- [12] Glasgow P. Simplicity: the ultimate sophistication. Br J Sports Med 2014;48:345.
- [13] Czuppon S, Racette BA, Klein SA, Harris-Hayes M. Variables associated with return to sport following anterior cruciate ligament reconstruction: a systematic review. Br J Sports Med 2014;48:356–64.
- [14] Yabroudi MA, Irrgang JJ. Rehabilitation and return to play after anatomic anterior cruciate ligament reconstruction. Clin Sports Med 2013;32:165–75.
- [15] Bischoff JE, Siggelkow E, Sieber D, Kersh M, Ploeg H, Munchinger M. Advanced material modeling in a virtual biomechanical knee. Abaqus Users' Conference; 2008.
- [16] Chizari M, Wang B. 3D numerical analysis of an ACL reconstructed knee. SIMULIA Customer Conference; 2009.
- [17] Tadeusiewicz R. How intelligent should be system for image analysis?. Berlin, Heidelberg, New York: Springer Verlag; 2011, V–X.
- [18] Pena E, Perez Del Palomar A, Calvo B, Martinez MA, Doblare M. Computational modeling of diarthrodial joints. Physiological, pathological and post-surgery simulations. Arch Comput Methods Eng 2007;14(1):47–91.
- [19] Li LP, Gu KB. Reconsideration on the use of elastic models to predict the instantaneous load response of the knee joint. Proc Inst Mech Eng H 2011;225(9):888–96.
- [20] Guess TM, Thiagarajan G, Kia M, Mishra M. A subject specific multibody model of the knee with menisci. Med Eng Phys 2010;32(5):505–15.
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- [22] Halloran JP, Petrella AJ, Rullkoetter PJ. Explicit finite element modeling of total knee replacement mechanics. J Biomech 2005;38:323–31.
- [23] Zarychta P. Posterior cruciate ligament – 3D visualization. In: Kurzynski M, et al., editors. Conference on Computer Recognition Systems, Advances in Intelligent and Soft Computing, vol. 45. Heidelberg: Springer-Verlag Berlin; 2007. p. 695–702.
- [24] Zarychta P. ACL and PCL of the knee joint in the computer diagnostics. 21st International Conference Mixed Design of Integrated Circuits and Systems MIXDES2014. 2014. pp. 489–92.
- [25] Badura P, Pietka E. Soft computing approach to 3D lung nodule segmentationin CT. Comput Biol Med 2014;53:230–43.
- [26] Badura P, Pietka E. Semi-automatic seed points selection in fuzzy connectedness approach to image segmentation. In: Kurzynski M, et al., editors. Conference on Computer Recognition Systems, Advances in Intelligent and Soft Computing, vol. 45. Heidelberg: Springer-Verlag Berlin; 2007. p. 679–86.
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- [29] Fisher R. The use of multiple measurements in taxonomic problems. Ann Eugen 1936;7(2):179–88.
- [30] Badura P. Accelerometric signals in automatic balance assessment. Comput Med Imaging Graph 2015;46:169–77.
- [31] Zarychta P, Konik H, Zarychta-Bargiela A. Computer assisted location of the lower limb mechanical axis. In: Pietka E, Kawa J, editors. Information technologies in biomedicine, Lecture Notes in Bioinformatics 7339. Heidelberg: Springer-Verlag Berlin; 2012. p. 93–100.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aa25e132-77a2-478d-b420-9199912961ea