Robust 2D Otsu's Algorithm for Uneven Illumination Image Segmentation

Abstract

Otsu's algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu's method is more robust compared to 1D Otsu's method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu's algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu's method, MAOTSU_2D, and two latest 1D Otsu's methods (Cao's method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%.