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Normal foot model is a geometric model of a healthy human foot. As the comparison of the processed feet requires a reference ideal healthy foot parameterization it was necessary to create such a model by defining skeleton geometric features and generating the feature set on a dataset population. Manual positioning of such number of landmarks is both a complex and time consuming task for a skilled radiologist, not to mention the total cost of such a procedure. Thus it was recommended to formulate an automated computer algorithm to perform this procedure with accuracy at a comparable level as the manual process. The following paper describes our approach based on automatic landmark positioning in a volumetric foot dataset. The proposed automated procedure is based on four main steps: manual landmark positioning on a reference dataset, registration of the reference dataset with the examined study, transformation of landmark positions from the reference dataset space into the examined dataset space, and calculation of the geometric features on the basis of landmarks positions. The results of our algorithm are presented and discussed in the context of pros and cons of the automated method itself as well as in the context of the generated normal foot model.
Wydawca
Rocznik
Tom
Strony
61--75
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Żwirki i Wigury 93, 02-089 Warsaw, Poland
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Żwirki i Wigury 93, 02-089 Warsaw, Poland
autor
- Faculty of Mathematics and Computer Science. N. Copernicus University, Chopina 12/18, 87-100 Toruń, Poland
autor
- Faculty of Mathematics and Computer Science. N. Copernicus University, Chopina 12/18, 87-100 Toruń, Poland
autor
- Carolina Medical Center, Pory 78, 02-757 Warsaw, Poland
autor
- Carolina Medical Center, Pory 78, 02-757 Warsaw, Poland
Bibliografia
- [1] Laskowski J., Adamczyk P., Zaorski P., Nurzynski P., Borucki B., Nowinski K., Normal foot – definition based on X-ray, MRI and CT imaging, to be published.
- [2] Venning P., Hardy RH., Sources of error in the production and measurement of standard radiographs of the foot, Br JR 24 (1951): 18.
- [3] Hardy R.H., Clapman J.C.R, Observations on hallux valgus; based on a controlled series, JBJS Br 33 (1951): 376.
- [4] Srivastava S. et al., Radiographic measurements of hallux angles: a review of current techniques, Foot 20 (2010).
- [5] Coughlin M.J., Mann R.A., Saltzman C.L., Surgery of the foot and ankle, Mosby, 8th Edition.
- [6] Davies A.M., Whitehouse R.M., Jenkins J.P.R., Imaging of the Foot & Ankle, Springer (2003).
- [7] Eustance S., Wiliamsoan D., Wilson M., Tendon shift in hallux valgus: observations at MR imaging, Skeletal Radiol. 25 (1996): 519.
- [8] Hajnal J.V., Hill D.L.G., Hawkes D.J., Medical Image Registration, CRC Press (2001).
- [9] Studholme C., Hill D.L.G, Hawkes D.J., An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition 32 (1999): 71.
- [10] Declerck J., Feldmar J., Goris M.L., Betting F., Automatic registration and alignment on a template of cardiac stress and rest reoriented SPECT images, IEEE Trans. Med. Imaging 6 (1997): 727.
- [11] Feldmar J., Declarck J., Malandian G., Ayache N., Extension of the ICP algorithm to non-rigid intensity-based registration of 3D volumes, Comp. Vision Image Understand. 66(2) (1997): 193.
- [12] Chlebiej M., Mikołajczak P., Nowiński K., Ścisło P., Bała P., Generation of dynamic heart model based on 4D echocardiographic images, LNCS, ICCSA2006, Springer-Verlag Berlin Heidelberg 5 (2006): 394.
- [13] Sederberg T, Parry S., Free form deformation of solid geometric models, Computer Graphics 20(4) (1986): 151.
- [14] Rueckert D., Sonoda L.I., Hayes C., Hill D.L. G., Leach M.O., Hawkes D.J., Non-rigid registration using free-form deformations: Application to breast MR images, IEEE Transactions on Medical Imaging 18(8) (1999): 712.
- [15] Shannon C.E., A mathematical theory of communication, Bell System Technical Journal 27 (1948): 2790423 and 623.
- [16] Modersitzki J., Numerical methods for image registration, Oxford University Press, chapter 8.1, (2004): 77.
- [17] Wahba G., Spline Models for Observational Data, BMS-NSF Regional Conference Series in Applied Mathematics 59, chapter 2.4, Philadelphia, PA (1990).
- [18] Lester H., Arridge S.R., A survey of hierarchical non-liner medical image registration, Pattern Recogn. 32(1) (1999): 129.
- [19] Jannin P., Grova C., Maurer C., Model for designing and reporting reference based validation procedures in medical image processing, Int. Journ. Comput. Assisted Radiol. and Surg. 1(2)2 (2006): 1001.
- [20] Jannin P., Korb W., Assessment of Image-Guided Interventions, Image Guided Interventions: Technology and Applications, Peters T. and Cleary K. (eds.), Springer (2008).
- [21] Vannier M.W., Lemke H.U. (eds.), Proceedings of the Computer Assisted Radiology and Surgery 2010, Springer (2010).
Typ dokumentu
Bibliografia
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