  The segmentation of digital images is one of the essential steps in image
processing or a computer vision system. It helps in separating the pixels into
different regions according to their intensity level. A large number of
segmentation techniques have been proposed, and a few of them use complex
computational operations. Among all, the most straightforward procedure that
can be easily implemented is thresholding. In this paper, we present a unique
heuristic approach for image segmentation that automatically determines
multilevel thresholds by sampling the histogram of a digital image. Our
approach emphasis on selecting a valley as optimal threshold values. We
demonstrated that our approach outperforms the popular Otsu's method in terms
of CPU computational time. We demonstrated that our approach outperforms the
popular Otsu's method in terms of CPU computational time. We observed a maximum
speed-up of 35.58x and a minimum speed-up of 10.21x on popular image processing
benchmarks. To demonstrate the correctness of our approach in determining
threshold values, we compute PSNR, SSIM, and FSIM values to compare with the
values obtained by Otsu's method. This evaluation shows that our approach is
comparable and better in many cases as compared to well known Otsu's method.
