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Proceedings Paper

Image analysis for beef quality prediction from serial scan ultrasound images
Author(s): Hui Lian Zhang; Doyle E. Wilson; Gene H. Rouse; Mercedes M. Izquierdo
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Paper Abstract

The prediction of intramuscular fat (or marbling) of live beef animals using serially scanned ultrasound images was studied in this paper. Image analysis, both in gray scale intensity domain and in frequency spectrum domain, were used to extract image features of tissue characters to get useful parameters for prediction models. One, 2 and 3 order multi-variable prediction models were developed from randomly selected data sets and tested using the remained data sets. The comparisons of prediction results between using serially scanned images and only final scanned ones showed good improvement of prediction accuracy. The correlation of predicted percent fat and actual percent fat increase from .68 to .80 and from .72 to .76 separately for two groups of data, the R squares increase from .65 to .68 and from .68 to .72, and the root of mean square errors decrease from 1.70 to 1.52 and from 1.22 to 1.12 separately. This study indicates that serially obtained ultrasound images from live beef animals have good potential for improving the prediction accuracy of percent fat.

Paper Details

Date Published: 6 January 1995
PDF: 6 pages
Proc. SPIE 2345, Optics in Agriculture, Forestry, and Biological Processing, (6 January 1995); doi: 10.1117/12.198895
Show Author Affiliations
Hui Lian Zhang, Iowa State Univ. (United States)
Doyle E. Wilson, Iowa State Univ. (United States)
Gene H. Rouse, Iowa State Univ. (United States)
Mercedes M. Izquierdo, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 2345:
Optics in Agriculture, Forestry, and Biological Processing
George E. Meyer; James A. DeShazer, Editor(s)

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