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With a rapid expansion of image segmentation over the decades, the growth of the mathematical optimization in the form of image thresholding is enormous on the segmentation. A need to organize the image thresholding arises to help medi-cal imaging, detection, and recognition in making an informed decision about the image. The proposed Rao algorithms are relied upon to quickly get the top-notch optimal thresholds are controlled by maximizing the Kapur entropy of various classes. Different from previous optimization techniques, Rao algorithms have been utilized as a prime optimization method as it has been ended up being a suc-cessful optimization when applied to different down to earth optimization issues and its execution is straightforward including less computational exertion. The technique has been tried on standard benchmark test images and the examination of the numerical outcome shows that this method is a promising option for the multilevel image thresholding issue
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