Segmentation of beef joint images using histogram thresholding

Four histogram-based thresholding methods, i.e., one-dimensional (1-D) histogram variance, 1-D histogram entropy, two-dimensional (2-D) histogram variance and 2-D histogram entropy, were proposed to segment the images of beef joints (raw, cooked and cooled) automatically from the background. The 2-D histogram-based methods incorporate a fast algorithm to reduce the calculation time, thus increasing the speed greatly. All the four methods were applied to 15 beef joint images captured from a video camera, and the methods including pixel classification, object overlap and object contrast for the evaluation of segmentation results were then employed to compare the performances or the abilities of the four different segmenting methods. Results indicate that the 2-D histogram variance thresholding method can accomplish the segmentation task with the most satisfactory performance.