Minimum Error Thresholding Segmentation Algorithm Based on 3D Grayscale Histogram

Threshold segmentation is a very important technique. The existing threshold algorithms do not work efficiently for noisy grayscale images. This paper proposes a novel algorithm called three-dimensional minimum error thresholding (3D-MET), which is used to solve the problem. The proposed approach is implemented by an optimal threshold discriminant based on the relative entropy theory and the 3D histogram. The histogram is comprised of gray distribution information of pixels and relevant information of neighboring pixels in an image. Moreover, a fast recursive method is proposed to reduce the time complexity of 3D-MET from to , where stands for gray levels. Experimental results demonstrate that the proposed approach can provide superior segmentation performance compared to other methods for gray image segmentation.