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An image segmentation system was developed for detecting the muscle longissimus thoraces (LT) in ultrasonic images of live pigs. The images have a low contrast, a high level of noise, and a high degree of variance in terms of texture and shape. The segmentation algorithm starts with a region growing process, which provides a rough approximation of the LT. Morphological operations and curve fitting eliminate unwanted noise. Finally, an active contour process refines the shape of the resulting region. This system takes several segmentation techniques and builds a flow of information between them but does not rely on specific a priori information of the texture or the contrast. This is a first step towards automating the loin detection in ultrasonic images oflive pigs. Initial experiments provided encouraging results. It is a modular system so that different region growing and refinement algorithms can be easily substituted into the current design. This makes for a general system that can be adapted to other segmentation tasks involving low contrast images. A series ofthree ultrasound images was made along the dorsal surface of 30 live pigs. Using the images to estimate loin volume, 64 and 70% of the variation in commercial loin weight and lean yield ofloin were predicted. Augmenting the model with backfat measurements, the R2 increased to 79 and 89%, respectively. These values compare to 76 and 79%, respectively, from measurements made on the carcass with the Hennessey Grading Probe. Keywords: low contract images, segmentation, region growing, morphology, curve fitting, snake.
A.W.K. Tong, R. Qureshi, X. Li and A.P. Sather 1998. A SYSTEM FOR ULTRASOUND IiYL4.GE SEGMENTATlO.' fOR LOIN EYE MEASURE 1ENTS I 'SWINE. Canadian Agricultural Engineering 40(1):47-53.
Canadian Society for Bioengineering