Distinguishing second harmonic generation images of mouse preterm labor via wavelet-based texture features

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2010, Pages 674-677

  Sobhi Afshar, A ^a   Kehtarnavaz, N ^a   Akins, M ^c   Luby Phelps, K ^b   Mahendroo, M ^c  

^a The University of Texas at Dallas   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^c UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

Image texture features; Preterm labor bio imaging application; Second harmonic generation imaging; Wavelet based texture features

Indexed keywords

COLLAGEN; HARMONIC ANALYSIS; HARMONIC GENERATION; IMAGE TEXTURE; MAMMALS; NONLINEAR OPTICS;

BIO-IMAGING; HARMONIC GENERATION IMAGING; IMAGE TEXTURE FEATURE; IMAGING APPLICATIONS; PRETERM LABOR; PRETERM LABOR BIO IMAGING APPLICATION; SECOND HARMONIC GENERATION IMAGING; TEXTURE FEATURES; WAVELET-BASED TEXTURE FEATURE;

BIOIMAGING;

EID: 78049368181     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2010.5495114     Document Type: Conference Paper

References (12)

1. Committee on Understanding Premature Birth and Assuring Healthy Outcomes, Preterm Birth: Causes, Consequences, and Prevention. Board on Health Sciences Policy, National Academies Press, 2006.
2. P. Leppert, "Anatomy and Physiology of Cervical Ripening," Clinical Obstetrics and Gynecology, vol. 38, no.2, pp. 267-279, 1995.
3. P. Campagnola, H. Clark, W. Mohler, A. Lewis and L. Leow, "Second harmonic imaging microscopy for visualizing biomolecular arrays in cells, tissues and organisms," Nature Biotech., vol. 21, pp. 1356-1360, 2003.
4. D. Dudley, D. Branch, S. Edwin, M. Mitchell, "Induction of preterm birth in mice by RU486," Biology of Reproduction, vol. 55, no. 5, pp. 992-995, 1996.
5. C. Raub, J. Unruh, V. Suresh, T. Krasieva, T. Lindmo, E. Gratton, B. Tromberg and S. George, "Image correlation spectroscopy of multiphoton images correlates with collagen mechanical properties," Biophys. J., vol. 94, pp. 2361-2373, March 2008.
6. R. Haralick, Computer and Robot Vision, Addison-Wesley, 1992.
7. S. Baeg, A. Popov, V. Kamat, S. Batman, K. Sivakumar, N. Kehtarnavaz, E. Dougherty, and R. Shah, "Segmentation of mammograms into distinct morphological texture regions," Proceedings Eleventh IEEE Symposium on Computer Based Medical Systems, pp. 20-25, Lubbock, Texas, 1998.
8. T. Wang, and N. Karayiannis, "Detection of microcalcifications in digital mammograms using wavelets," IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 498-509, 1998.
9. G. de Wouwer, P. Scheunders, and D. Van Dyck, "Statistical texture characterization from discrete wavelet representations," IEEE Transactions on Image Processing, vol. 8, no.4, pp. 592-598, 1999.
10. Y. Zhang, X.-J. HE, and J.-H. Han, "Texture feature-based image classification using wavelet package transform," Lecture Notes in Computer Science, vol. 3644, pp. 165-173, Sept. 2005.
11. S. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Trans. on PAMI, vol. 11, no. 7, pp. 674-693, 1989.
12. R. Duda, P. Hart, D. Stork, Pattern Classification, Wiley-Interscience, 2000.