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

Abstract

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 ∂Ω by mech-f-EIT for base data, 2) approximation of Jacobian matrix J* by k-nearest neighbor (k-NN) algorithm under ∂Ω for high speed, 3) clustering of meat composition kσ (fat k = 1, lean k = 2, bone k = 3) by fuzzy k-means algorithm based on the reconstructed meat conductivity distribution σ for high accuracy, and 4) edge detection of meat composition kΩ 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 ∂Ω with total mean boundary error 〈ẽb〉 = 4.81 %. This method achieves high-speed approximation of J* with total mean speed-up performance 〈s̃p〉 = 4.51 times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate 〈c̃c〉 = 0.92. Moreover, this method clusters the kσ with total mean area error 〈ẽa〉 = 4.49 %. Furthermore, this imaging method detects sharply the meat composition edges kΩ between fat and lean (k = 1 - 2) and between lean and bone (k = 2 - 3) with total mean edge error 〈ẽe〉 =6.90 %.

Keywords

• Electrical impedance tomography
• k-nearest neighbor
• fuzzy k-means
• meat composition imaging