High-Speed and Accurate Meat Composition Imaging by Mechanically-Flexible Electrical Impedance Tomography With k -Nearest Neighbor and Fuzzy k -Means Machine Learning Approaches

High-speed and accurate meat composition imaging method has been proposed based on mechanically-flexible electrical impedance tomography ( mech-f- EIT) with $k$ -nearest neighbor and fuzzy $k$ -means machine learning approaches. This proposed method has four stages which are 1) estimation of meat boundary shape $\partial \Omega $ by mech-f- EIT for base data, 2) approximation of Jacobian matrix J* by $k$ -nearest neighbor ( $k$ -NN) algorithm under $\partial \Omega $ for high speed, 3) clustering of meat composition $^{k} \boldsymbol {\sigma }$ (fat $k=1$ , lean $k=2$ , bone $k=3$ ) by fuzzy $k$ -means algorithm based on the reconstructed meat conductivity distribution $\sigma $ for high accuracy, and 4) edge detection of meat composition $^{k}\Omega $ by Canny algorithm for sharp edge. This method is qualitatively evaluated by using two agar phantoms, a cow’s lower leg and three lamb’s lower legs. As the results, mech-f- EIT estimates $\partial \Omega $ with total mean boundary error $\langle \widetilde {e_{b}} \rangle =4.81$ %. This method achieves high-speed approximation of J* with total mean speed-up performance $\langle \widetilde {sp} \rangle =4.51$ times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate $\langle \widetilde {cc} \rangle =0.92$ . Moreover, this method clusters the $^{k} \boldsymbol {\sigma } $ with total mean area error $\langle \widetilde {e_{a}} \rangle =4.49$ %. Furthermore, this imaging method detects sharply the meat composition edges $^{k}\Omega $ between fat and lean ( $k=1-2$ ) and between lean and bone ( $k=2-3$ ) with total mean edge error $\langle \widetilde {e_{e}} \rangle =6.90$ %.