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Towards automatic interpretation of sheep ultrasound scans

Published online by Cambridge University Press:  02 September 2010

C. A. Glasbey ,
I. Abdalla  and
G. Simm
C. A. Glasbey
Affiliation:
Biomathematics and Statistics Scotland, King's Buildings, Edinburgh EH9 3JZ
I. Abdalla
Affiliation:
Department of Mathematics and Statistics, University of Edinburgh, King's Buildings, Edinburgh EH9 3JZ
G. Simm
Affiliation:
Genetics and Behavioural Sciences Department, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG
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Abstract

Ultrasound imaging is widely used in animal breeding to provide in vivo estimates of the carcass composition of candidates for selection. Although the technique is less accurate than more recent medical imaging methods, such as X-ray computed tomography and magnetic resonance imaging, it is relatively cheap and mobile. Therefore large numbers of animals can be measured. Most current ultrasound scanners require some degree of manual interpretation of images, which is time consuming and liable to vary both between and within individuals. Hence, this study investigated the automatic interpretation of ultrasound scans from sheep. A computer algorithm is proposed for identifying tissue boundaries. Estimates of tissue depth are shown to be comparable with those obtained by manual interpretation, for images of 72 sheep scanned twice at the position of the 13th thoracic vertebra. The root-mean-square errors of subcutaneous fat depth and m. longissimus muscle depth ivere 0·7 mm and 1·7 mm, respectively.

Keywords

carcass compositionfatlongissimus dorsisheepultrasound
Type
Research Article
Information
Animal Science , Volume 62 , Issue 2 , April 1996 , pp. 309 - 315
Copyright
Copyright © British Society of Animal Science 1996

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References

REFERENCES

Abdalla, I. 1994. Statistical image analysis of sheep ultrasound scans, M. Phil. Thesis, University of Edinburgh.Google Scholar
Alliston, J. C. 1983. Evaluation of carcass quality in the live animal. In Sheep production (ed. Haresign, W.), pp. 75–95. Butterworths, London,Google Scholar
Amit, Y., Grenander, U. and Piccioni, M. 1991. Structural image restoration through deformable templates. journal of the American Statistical Association 86: 376–387.CrossRefGoogle Scholar
Baldock, R. A. 1992. Trainable models for the interpretation of biomedical images. Image and Vision Computing 10: 444–450.CrossRefGoogle Scholar
Cootes, T. F., Hill, A., Taylor, C. J. and Haslam, J. 1994. Use of active shape models for locating structure in medical images. Image and Vision Computing 12: 355–365.CrossRefGoogle Scholar
Glasbey, C. A. and Horgan, G. W. 1995. Image analysis for the biological sciences. Wiley, Chichester.Google Scholar
Leavers, V. F. 1992. Shape detection in computer vision using the Hough transform. Springer-Verlag, London.CrossRefGoogle Scholar
Lin, W. J., Pizer, S. M. and Johnson, V. E. 1991. Boundary estimation in ultrasound images. In Information processing in medical imaging. Proceedings of the twelfth international conference on information processing in medical imaging (ed. Colchester, A. C. F. and Hawkes, D. J.), pp. 285–299. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Loupas, T., McDicken, W. N. and Allan, P. L. 1989. An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Transactions on Circuits and Systems 36: 129–135.CrossRefGoogle Scholar
Miles, C. A., Fursey, G. A. J. and York, R. W. R. 1984. New equipment for measuring the speed of ultrasound and its application in the estimation of body composition of farm livestock. In In vivo measurement of body composition in meat animals (ed. Lister, D.), pp. 93–105. Elsevier, London.Google Scholar
Miles, C. A., Pomeroy, R. W. and Harries, J. M. 1972. Some factors affecting reproducibility in ultrasonic scanning of animals. 1. Cattle. Animal Prduction 15: 239–249.Google Scholar
Moshfeghi, M. 1991. Elastic matching of multimodality medical images. Computer Vision, Graphics and Image Processing — Graphical models and image processing 53: 271–282.Google Scholar
Simm, G. 1983. The use of ultrasound to predict the carcass composition of live cattle — a review. Animal Breeding Abstracts 51: 853–875.Google Scholar
Simm, G. 1987. Carcass evaluation in sheep breeding programmes. In New techniques in sheep production (ed. Marai, I. F. M. and Owen, J. B.), pp. 125–144. Butterworths, London.CrossRefGoogle Scholar
Simm, G. 1992. Selection for lean meat production in sheep. In Progress in sheep and goat research (ed. Speedy, A. W.), pp. 193–215. CAB International, Wallingford.Google Scholar
Simm, G., Dingwall, W. S., Murphy, S. V. and FitzSimons, J. 1990. Selection for improved carcass composition in Suffolk sheep. Proceedings of the fourth world congress on genetics applied to livestock production, vol. XV, pp. 100–103.Google Scholar