超音波エコー動画像に基づく肉牛の脂肪交雑値推定
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概要
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Up to the present time, estimation of Beef Marbling Standard (BMS) number based on ultrasound echo imaging of live beef cattle has been studied. However, previous attempts to establish the objective and high accurate estimation method have not been satisfactory. Our previous work showed that estimation of BMS number was achieved by neural network modeling with non-linear mapping ablity. This paper reports a significant improvement of the estimation method based on dynamic ultrasound image. The proposed method consists of four processes: the extraction of dynamic and static texture features, frequency analysis, principal component analysis, and the estimation of BMS number by neural network. In order to evaluate the effectiveness of the proposed method, the experiments were conducted with or without dynamic image information. The number of target regions was set to 1 or 2, and two groups of samples, Case 1 and Case 2, were used for the experiments. Case 1 and Case 2 included 18 and 27 samples, which were measured at Saga Livestock Experiment Station and Nagasaki Agricultural and Forestry Technical Development Center, respectively. The image analysis was performed using only Case 1 or using the mixed group of Case 1 and 2. The experimental results with Case 1 showed the correlation coefficient of the estimated and the actual BMS number was improved from r=0.55 to r=0.79 by adding dynamic image information. Moreover, the correlation coefficient was further raised to r=0.84 with the number of target region increased from 1 to 2. Similarly, as for the mixed group of Case 1 and 2, the correlation coefficients were r=0.77, r=0.76, and r=0.88, respectively. These results suggested that a high estimation accuracy was achieved by adding dynamic image information and increasing target region.
- 公益社団法人 計測自動制御学会の論文
公益社団法人 計測自動制御学会 | 論文
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