Double regularization control based on level set evolution for tablet packaging image segmentation

Abstract

This paper proposes a novel double regularization control (DRC) method which is used for tablet packaging image segmentation. Since the intensities of tablet packaging images are inhomogenous, it is difficult to make image segmentation. Compared to methods based on level set, the proposed DRC method has some advantages for tablet packaging image segmentation. The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate. Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation. The developed penalty energy function, as regularization term, increases the constrained conditions based on the gradient flow conditions. Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended, the proposed method further improves the accuracy of image contours. Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy, and achieves better results for image contour segmentation compared to other level set methods.