Histogram feature-based classification improves differentiability of early bone healing stages from micro-computed tomographic data

Journal of Computer Assisted Tomography

Volumn 36, Issue 4, 2012, Pages 469-476

  Preininger, Bernd ^a   Hesse, Bernhard ^a,b   Rohrbach, Daniel ^a   Varga, Peter ^a   Gerigk, Hinnerk ^a   Langer, Max ^b,c   Peyrin, Francoise ^b,c   Perka, Carsten ^a   Raum, Kay ^a  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^b EUROPEAN SYNCHROTRON RADIATION FACILITY   (France)

^c UNIVERSITÉ DE LYON   (France)

Author keywords

bone healing; histogram moment; microYcomputed tomography

Indexed keywords

BONE; COMPUTERIZED TOMOGRAPHY; GRAPHIC METHODS; HISTOLOGY; SENSITIVITY ANALYSIS;

BONE HEALING; COMPUTED TOMOGRAPHIC; CONNECTIVE TISSUES; DIFFERENTIABILITY; FEATURE-BASED CLASSIFICATION; HISTOGRAM FEATURES; HISTOGRAM MOMENTS; MICROYCOMPUTED TOMOGRAPHY; MINERALIZED TISSUE; TOMOGRAPHIC DATA;

COLLAGEN;

ACCURACY; ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CALLUS; CLINICAL CLASSIFICATION; CLINICAL FEATURE; FEASIBILITY STUDY; FRACTURE HEALING; HISTOGRAM; IN VIVO STUDY; LONG BONE; MICRO-COMPUTED TOMOGRAPHY; NONHUMAN; OSTEOTOMY; PILOT STUDY; PRIORITY JOURNAL; RAT; SENSITIVITY ANALYSIS; TIME;

EID: 84863895042     PISSN: 03638715     EISSN: 15323145     Source Type: Journal    
DOI: 10.1097/RCT.0b013e31825eae8a     Document Type: Article

References (32)

1. Langer M, Liu Y, Tortelli F, et al. Regularized phase tomography enables study of mineralized and unmineralized tissue in porous bone scaffold. J Microsc. 2010;238:230-239.
2. Momose A. Recent advances in X-ray phase imaging. Jpn J Appl Phys. 2005;44:6355-6367. (Pubitemid 43160597)
3. Zhou SA, Brahme A. Development of phase-contrast X-ray imaging techniques and potential medical applications. Phys Med. 2008;24:129-148.
4. Donath T, Pfeiffer F, Bunk O, et al. Toward clinical X-ray phase-contrast CT: demonstration of enhanced soft-tissue contrast in human specimen. Invest Radiol. 2010;45:445-452.
5. Peters A, Schell H, Bail HJ, et al. Standard bone healing stages occur during delayed bone healing, albeit with a different temporal onset and spatial distribution of callus tissues. Histol Histopathol. 2010;25:1149-1162.
6. Protopappas VC, Vavva MG, Fotiadis DI, et al. Ultrasonic monitoring of bone fracture healing. IEEE Trans Ultrason Ferroelectr Freq Control. 2008;55:1243-1255.
7. Muller R, Ruegsegger P. Micro-tomographic imaging for the nondestructive evaluation of trabecular bone architecture. Stud Health Technol Inform. 1997;40:61-79.
8. Muller R, Van Campenhout H, Van Damme B, et al. Morphometric analysis of human bone biopsies: a quantitative structural comparison of histological sections and micro-computed tomography. Bone. 1998;23:59-66. (Pubitemid 28327502)
9. Burghardt AJ, Kazakia GJ, Majumdar S. A local adaptive threshold strategy for high resolution peripheral quantitative computed tomography of trabecular bone. Ann Biomed Eng. 2007;35:1678-1686. (Pubitemid 47444139)
10. Buie HR, Campbell GM, Klinck RJ, et al. Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. Bone. 2007;41:505-515. (Pubitemid 47385446)
11. Freeman TA, Patel P, Parvizi J, et al. Micro-CT analysis with multiple thresholds allows detection of bone formation and resorption during ultrasound-treated fracture healing. J Orthop Res. 2009;27:673-679.
12. Morgan EF, Mason ZD, Chien KB, et al. Micro-computed tomography assessment of fracture healing: relationships among callus structure, composition, and mechanical function. Bone. 2009;44:335-344.
13. Duvall CL, Taylor WR, Weiss D, et al. Quantitative microcomputed tomography analysis of collateral vessel development after ischemic injury. Am J Physiol. 2004;287:302-310.
14. Preininger B, Gerigk H, Bruckner J, et al. An experimental setup to evaluate innovative therapy options for the enhancement of bone healing using BMP2 as a benchmark-a pilot study. Eur Cell Mater. 2012;23:262-272.
15. Mehta M, Strube P, Peters A, et al. Influences of age and mechanical stability on volume, microstructure, and mineralization of the fracture callus during bone healing: is osteoclast activity the key to age-related impaired healing? Bone. 2010;47:219-228.
16. Lilliefors HW. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc. 1967;62:399-402.
17. Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273-297.
18. Chih-Wei H, Chih-Jen L. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw. 2002;13:415-425.
19. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory. 1967;13:21-27.
20. Mitchell T. Machine Learning. New York, NY: MacGraw-Hill; 1997.
21. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. San Francisco, CA: Morgan Kaufmann Publishers Inc; 1995:1137-1143.
22. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006;27:861-874.
23. Everson RM, Fieldsend JE. Multi-class ROC analysis from a multi-objective optimisation perspective. Pattern Recogn Lett. 2006;27:918-927.
24. Fernandez Caballero JC, Martinez FJ, Hervas C, et al. Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks. IEEE Trans Neural Netw. 2010;21:750-770.
25. Raudys SJ, Jain AK. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell. 1991;13:252-264.
26. Hua J. Optimal number of features as a function of sample size for various classification rules. Bioinformatics. 2004;21:1509-1515. (Pubitemid 40552752)
27. Meyer D. The support vector machine under test. Neurocomputing. 2003;55:169-186. (Pubitemid 37336675)
28. Theodoridis S. Pattern Recognition. Amsterdam. Amsterdam; Boston: Elsevier/Academic Press; 2006.
29. El-Naqa I, Yongyi Y, Wernick MN, et al. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002;21:1552-1563.
30. Zhang Z, Zhang S, Zhang C-X, et al. SVM for density estimation and application to medical image segmentation. J Zhejiang University Science B. 2006;7:365-372.
31. Duda R. Pattern Classification. New York, NY: Wiley; 2001.
32. Patrick E, Fischeriii F. A generalized nearest neighbor rule. Information and Control. 1970;16:128-152.