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RESEARCH ARTICLE
Contents Vol 19(8)

Enhancing ultrasound texture differences for developing an in vivo ‘virtual histology’ approach to bovine ovarian imaging

Mark G. Eramian A D , Gregg P. Adams B and Roger A. Pierson C
+ Author Affiliations
- Author Affiliations

A Department of Computer Science, The University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

B Department of Veterinary Biomedical Sciences, The University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

C Department of Obstetrics, Gynecology and Reproductive Sciences, The University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

D Corresponding author. Email: eramian@cs.usask.ca

Reproduction, Fertility and Development 19(8) 910-924 https://doi.org/10.1071/RD06167
Submitted: 13 December 2006  Accepted: 22 July 2007   Published: 11 September 2007

Abstract

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

Additional keywords: ovary, sticks filter, texture analysis.


Acknowledgements

This research was supported by grants from the Saskatchewan Heath Research Foundation, the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research. We wish to thank Dr J. Singh, Department ofVeterinary Biomedical Sciences, University of Saskatchewan, for providing the image in Fig. 9.


References

Abolmaesumi R., and Sirouspour M. R. (2004). Segmentation of prostate contours from ultrasound images. In ‘Proceedings of the International Conference on Acoustics, Speech and Signal Processing’ pp. 517–520.

Adams, G. P. , and Pierson, R. A. (1995). Bovine model for study of ovarian follicular dynamics in humans. Theriogenology 43, 113–120.
| Crossref | GoogleScholarGoogle Scholar | Bylund N. E., Ressner M., and Knutsson H. (2003). 3D wiener filtering to reduce reverberations in ultrasound image sequences. In ‘Proceedings of the 13th Scandinavian Conference on Image Analysis (SCIA), Volume 2749 of Lecture Notes in Computer Science’. (Eds G. Goos, J. Hartmanis and J. van Leeuwen.) pp. 579–586. (Springer-Verlag: Berlin.)

Czerwinski, R. N. , Jones, D. L. , and O’Brien, W. D. (1998). Line and boundary detection in speckle images. IEEE Trans. Image Process. 7, 1700–1714.
| Crossref | GoogleScholarGoogle Scholar | Eramian M. G., Pierson R. A., and Adams G. P. (2006). ‘Sticks Filtering of Ovarian Images.’ Technical Report 2006–04. (Department of Computer Science, The University of Saskatchewan: Saskatoon.)

Foley J. D., van Dam A., Feiner S. K., Hughes J. F., and Phillips R. L. (1994). ‘Introduction to Computer Graphics.’ (Addision-Wesley: Reading, MA.)

Gupta, S. , Chauhan, R. C. , and Sexana, S. C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Comput. 42, 189–192.
| Crossref | GoogleScholarGoogle Scholar | PubMed | Jain A. K. (1989). ‘Fundamentals of Digital Image Processing.’ (Prentice-Hall: Englewood Cliffs, NJ.)

Kiesslich, R. , and Neurathman, M. F. (2004). Review: potential of new endoscopic techniques: intravital staining and in vivo confocal endomicroscopy for the detection of premalignant lesions and early cancer in patients with ulcerative colitis. Acta Endosc. 34, 189–198.
Ritenour E. R., Nelson T. R., and Raff U. (1984). Application of median filter to digital radiographic images. In ‘Proceedings of the 7th International Conference on Acoustics, Speech, and Signal Processing’. pp. 23.1.1–23.1.4. (IEEE: New York.)

Sakashita, M. , Inoue, H. , Kashida, H. , Tanaka, J. , and Cho, J. Y. , et al. (2003). Virtual histology of colorectal lesions using laser-scanning confocal microscopy. Endoscopy 35, 1033–1038.
| Crossref | GoogleScholarGoogle Scholar | PubMed | Sonka M., Hlavac V., and Boyle R. (1999). ‘Image Processing, Analysis, and Machine Vision.’ (PWS Publishing: Pacific Grove, CA.)

Tomasi C., and Manduchi R. (1998). Bilateral filtering for gray and color images. In ‘Proceedings of the IEEE International Conference on Computer Vision’. pp. 836–846. (IEEE: New York.)

Yu, Y. , and Acton, S. T. (2002). Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11, 1260–1270.
| Crossref | GoogleScholarGoogle Scholar |