Noisy image segmentation is one of the most important and challenging problem in computer vision. In this paper, we propose a level set segmentation technique inspired by the classic Otsu thresholding method. The front propagation of the proposed level set based method embeds a cost function that takes into account first-order statistical moments. In order to deal with highly noisy images, we also added a morphological step to our algorithm which led the final segmentation more robust and efficient. Tests were carried out on images artificially contaminated with Gaussian and Salt & Pepper noise patterns. The results showed that our methodology outperformed the classic Otsu thresholding algorithm and an active contour based technique in terms of the Error of Segmentation (EoS), Rate of False Positive (RFP), Rate of False Negative (RFN) and Dice evaluation measures. In addition, the designed algorithm attained a lower average computational time when compared to the active contour related method.