A modified Otsu-based image segmentation algorithm (OBISA)

Abstract

Image thresholding is usually a preprocessing step in a number of image processing algorithms. The segmented images are input for image analyses, computer vision, and visualizations and object representation. Otsu thresholding method is a widely used image thresholding technique. It provides fairly accurate results for segmenting a gray level image with only one modal distribution in a gray level histogram. However, one of the drawbacks is high computational cost and noise that are mostly contributed by inappropriate expression of class statistical distributions. This paper presents an improved Otsu-based image segmentation algorithm to enhance the performance of the Otsu method. Standard deviation is used in the computation of the optimal threshold instead of using variance. A reasonable threshold range is computed to lower the computational cost. Testing results showed that the improved method is more satisfactory than the original Otsu thresholding algorithm.