Learning Fourier Descriptors for Computer-Aided Diagnosis of the Supraspinatus

Academic Radiology

Volumn 17, Issue 8, 2010, Pages 1040-1049

  van Kaick, Oliver ^a   Hamarneh, Ghassan ^a   Ward, Aaron D ^b   Schweitzer, Mark ^c   Zhang, Hao ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b ROBARTS RESEARCH INSTITUTE   (Canada)

^c UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; FOURIER ANALYSIS; FOURIER TRANSFORMATION; HUMAN; IMAGE ANALYSIS; MAJOR CLINICAL STUDY; MUSCLE INJURY; MUSCLE MASS; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PRIORITY JOURNAL; ROTATOR CUFF; SHOULDER DISEASE; SHOULDER PAIN; SUPRASPINATUS INJURY; SUPRASPINATUS MUSCLE; THREE DIMENSIONAL IMAGING;

DIAGNOSIS, COMPUTER-ASSISTED; FOURIER ANALYSIS; HUMANS; MAGNETIC RESONANCE IMAGING; MUSCULAR DISEASES; SHOULDER;

EID: 77954218292     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2010.04.006     Document Type: Article

References (28)

1. Fuchs S., Chylarecki C., Langenbrinck A. Incidence and symptoms of clinically manifest rotator cuff lesions. Int J Sports Med 1999, 20:201-205.
2. Lehman C., Cuomo F., Kummer F.J., et al. The incidence of full thickness rotator cuff tears in a large cadaveric population. Bull Hosp Jt Dis 1995, 54:30-31.
3. Zanetti M., Gerber C., Hodler J. Quantitative assessment of the muscles of the rotator cuff with magnetic resonance imaging. Invest Radiol 1998, 33:163-170.
4. Morag Y., Jacobson J.A., Miller B., et al. MR imaging of rotator cuff injury: what the clinician needs to know. Radiographics 2006, 26:1045-1065.
5. Ward A., Hamarneh G., Ashry R., et al. 3D shape analysis of the supraspinatus muscle: a clinical study of the relationship between shape and pathology. Acad Radiol 2007, 14:1229-1241.
6. Ashburner J., Csernansk J.G., Davatzikos C., et al. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2003, 2:79-88.
7. Wang L., Swank J.S., Glick I.E., et al. Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. NeuroImage 2003, 20:667-682.
8. Gutman B., Wang Y., Lui L.M., et al. Hippocampal surface analysis using spherical harmonic function applied to surface conformal mapping. Int Conf Pattern Recognition 2006, 3:964-967.
9. Chung M.K., Nacewicz B.M., Wang S., et al. Amygdala surface modeling with weighted spherical harmonics. Lect Notes Computer Sci 2008, 5128:177-184.
10. Nain D., Styner M., Niethammer M., et al. Statistical shape analysis of brain structures using spherical wavelets. Int Symp Biomed Imaging 2007, 1:209-212.
11. Wang S., Chung M.K., Dalton K.M., et al. Automated diagnosis of autism using Fourier series expansion of corpus callosum boundary. Human Brain Mapping Conf 2007, 1.
12. Chen S.Y.Y., Lestrel P.E., Kerr W.J.S., et al. Describing shape changes in the human mandible using elliptical Fourier functions. Eur J Orthodont 2000, 22:205-216.
13. Tsai A., Wells W.M., Warfield S.K., et al. An EM algorithm for shape classification based on level sets. Med Image Anal 2005, 9:491-502.
14. Heimann T., Meinzer H.-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 2009, 13:543-563.
15. Gotsman C., Gu X., Sheffer A. Fundamentals of spherical parameterization for 3D meshes. ACM Trans Graphics (Proc. SIGGRAPH) 2003, 22:358-363.
16. Staib H., Duncan J.S. Boundary finding with parametrically deformable models. Trans Patt Anal Machine Intell 1992, 14:1061-1075.
17. Hamarneh G., Gustavsson T. Statistically constrained snake deformations. Proc Int Conf Syst Man Cybernetics 2000, 3:1610-1615.
18. Lehtinen J.T., Tingart M.J., Apreleva M., et al. Practical assessment of rotator cuff muscle volumes using shoulder MI. Acta Orthop Scand 2003, 74:722-729.
19. Tingart M.J., Apreleva M., Lehtinen J.T., et al. Shoulder magnetic resonance imaging in quantitative analysis of rotator cuff muscle volume. Clin Orthopaed Related Res 2003, 415:104-110.
20. Robertson P.L., Schweitzer M.E., Mitchell D.G., et al. Rotator cuff disorders: Interobserver and intraobserver variation in diagnosis with MR imaging. Radiology 1995, 194:831-835.
21. Turk G., O'Brien J.F. Shape transformation using variational implicit functions. Proc ACM SIGGRAPH 1999, 1:335-342.
22. Wu T.-F., Lin C.-J., Weng R.C. Probability estimates for multi-class classification by pairwise coupling. J Machine Learn Res 2004, 5:975-1005.
23. Guyon I., Elisseeff A. An introduction to variable and feature selection. J Machine Learn Res 2003, 3:1157-1182.
24. Vapnik V. Statistical Learning Theory 1998, Wiley, New York.
25. Sun Y., Kamel M.S., Wang Y., et al. Boosting for learning multiple classes with imbalanced class distribution. Proc. Int Conf Data Mining 2006, 1:592-602.
26. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett 2006, 27:861-874.
27. Sokolova M. Assessing invariance properties of evaluation measures. Proc NIPS Workshop on Testing Deployable Learning and Decision Systems 2006.
28. Skurichina M., Verzakov S., Paclik P., et al. Effectiveness of spectral band selection/extraction techniques for spectral data. Lecture Notes Computer Sci 2006, 4109:541-550.