Determining thresholds by measuring class variance is highly effective for image segmentation. Otsu’s method and its derivatives are common approaches that are both simple and adaptable. In spite of these methods’ excellent segmentation performance, images with particular gray distributions cause a thresholding bias that limits their usefulness. We explore the limitations of Otsu’s method and apply other evaluation criteria. In particular, we determine the relative homogeneity between the object and the background and then use it as a classification criterion along with a new binary thresholding method. Our method employs a histogram-smoothing method to improve valley-point selection, establishes a uniformity measure to identify the region with the best homogeneity, and identifies an optimization function for obtaining the best values for the adjustable parameters and threshold value. We also introduce a multilevel thresholding criterion based on a binary thresholding approach. Experiments using real and ground truth test images confirm the validity of our proposed method. Our method also offers a denoising ability when configured to use neighborhood information.
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The work is supported by the National Science Foundation of China (Nos. 61571361, 61671377), the Science Plan Foundation of the Education Bureau of Shaanxi Province (No. 15JK1682), and Scientific Research Climbing Project of Xiamen University of Technology, No. XPDKT18016.
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Zhang, H., Chiu, Y. & Fan, J. A novel relative homogeneity thresholding method with optimization strategy. Neural Comput & Applic 32, 8431–8449 (2020). https://doi.org/10.1007/s00521-019-04333-3
- Thresholding segmentation
- Relative homogeneity
- Optimization strategy
- Multilevel thresholding