A Local Thresholding Algorithm for Image Segmentation by Using Gradient Aided Histogram

In image segmentation, local thresholding algorithms may yield more accurate and robust results since they are based on the features of images. Therefore, the common patterns exhibit in the same image category is crucial to improve the quality of segmentation results. In present paper, a new local thresholding algorithm that using gradient aided histogram is proposed to process the images that have apparent texture or periodical structure. It is found that clustering pixels with similar gray-level gradient plays an important role for the multi-level image segmentation. The famous global thresholding algorithms, such as Kapur and Otsu, are adopted to make the comparison. The results are quantitatively illustrated in terms of PSNR (Peak Signal-to-Noise Ratio) and FSIM (Feature Similarity Index). It is shown that the proposed algorithm can effectively recognize the common features of the images that belong to the same category, and maintain the stable performances when the number of threshold increases. Furthermore, the processing time of present algorithm is competitive to those of other algorithms, which shows the potential application in real time scenes.