Estimation of the marbled score in live beef cattle based on ICA and US texture information

为了准确估计牛肉大理石花纹标准（BMS）的活牛使用超声回波成像的数量，我们已经开发了一种图像识别方法，通过使用神经网络。我们研究的效率应用独立分量分析（伊卡）的多维图像特征的压缩。我们已经实现了估计测试，通过使用超声回波成像测量从活牛。基于交叉验证方法对估计精度进行了评价。结果表明，伊卡法的实际值与估计值的相关系数（R=0.70，p<0.01）高于PCA法（R=0.62，p<0.01）。此外，我们进行了比较实验之间的伊卡为基础的估计和估计的经验丰富的检查员。这两种方法检查相同的超声图像。因此，我们证实，所提出的方法具有相同的能力，有经验的检查员估计BMS数量。

To accurately estimate the Beef Marbling Standard(BMS) number of live cattle using ultrasound echo imagings, we have developed a image recognition method by use of a neural network. We examine the efficiency of applying Independent Component Analysis(ICA) to the compression of multidimensional image features. We have implemented the estimation tests by use of ultrasound echo imagings measured from live cattles. The estimation accuracy was evaluated based on the cross validation method. The results confirmed that the correlation coefficient between the actual and the estimated values was higher by ICA (R=0.70, p<0.01) than by PCA (R=0.62, p<0.01). Also, we conducted the comparison experiments between the ICA based estimation and the estimation by an experienced inspector. The both methods examined the same ultrasound images. As a result, we confirmed that the proposed method had much the same capability as the experienced inspector to estimate BMS number.