Image segmentation is the process of partitioning a digital image into multiple segments that makes the image more meaningful and easier to analyze. It is generally used to locate objects and boundaries (lines, curves, etc.) in images. Image thresholding is one of the image segmentation methods, it converts the grayscale image into a binary image. Variational minimax optimization is one of the best methods used for image thresholding [1] [5-9]. In this article, I would study the performance of this algorithm for a noisy grayscale image. For this, I consider an image processing system model which is a logical block diagram of the processes involved in this performance study. The performance will however be in terms of image similarity observed between the original binary image and the denoised but degraded binary image obtained using the image threshold algorithm mentioned above. Image similarity or image quality is represented by the universal image quality index [2] which will differ for different SNR values ​​for the noisy grayscale image. Finally, the results are compiled and conclusions are drawn.1. IntroductionIn many image processing applications, the gray levels of pixels belonging to the object or foreground are very different from the gray levels of pixels belonging to the background. Thresholding then becomes a simple tool to separate the foreground from the background. Examples of thresholding applications are document image analysis where the objective is to extract logos from printed characters, processing of graphics cards where there are lines, legends, characters, inspection quality of materials, etc. [3]. The result of the thresholding operation is a binary result. image whose gray level of 0 (black) will indicate...... middle of paper...... Variational image threshold N. Ray, BN Saha Alberta Univ., Edmonton Proceedings / ICIP ... Conference international image processing 01/2007; 6:VI - 37 - VI - 40. DOI:10.1109/ICIP.2007.4379515 ISBN: 978-1-4244-1437-6 Following on from: Image Processing, 2007. ICIP 2007. IEEE International Conference on, Volume : 6[6 ]UN. Ruszczynski, Nonlinear Optimization, Princeton University Press, Princeton, NJ, 2006.[7] Brown, Robert Grover; Hwang, Patrick YC (1996). Introduction to Random Signals and Applied Kalman Filtering (3 ed.). New York: John Wiley & Sons. ISBN 0-471-12839-2.[8] The Handbook of Color Image Processing by Sangwine, Stephen J.; Horne, Robin EN (Eds.)1998, XV, 440 p[9] Thomos, N., Boulgouris, NV and Strintzis, MG (January 2006). Optimized transmission of JPEG2000 streams over wireless channels. IEEE Transactions on Image Processing , 15 (1).