A Novel Percentage Split Distribution Method for Image Thresholding

Traditional image thresholding methods are found to be effective for bi-level thresholding, but computationally expensive for multi-level thresholding. Most of these methods do not precisely segment images with zero root mean square error. Nevertheless these methods fail to select quality thresholds with the increased number of segmentations. To overcome these issues, this paper proposes a novel and fastest image thresholding method 'percentage split distribution' to segment images into regions of relevant groups. The method effectively computes the globally optimized thresholds through percentage-wise segmentation of image pixels. To analyze the performance of the proposed method, nine bench mark grayscale images are taken and validated against SSIM, PSNR and time complexity measures. The experimental results show that the proposed method is superior to other thresholding based image segmentation methods in terms of high PSNR and SSIM values resulted with reduced time complexity. The experimental results also proclaim that the thresholds generated by the proposed method are effective and are of equal intervals. Moreover, the proposed method is found to be effective, simple, easy to implement and can be effectively used for real-time applications.