Uncertainty Avoider Defuzzification of General Type-2 Multi-Layer Fuzzy Membership Functions for Image Segmentation

General type two fuzzy models are rarely used in practical problems due to their computational complexity, especially during fuzzification and defuzzification. This is why the main purpose of this article is to present a simple and effective method with a closed formula to increase their use in real-time tasks. For this purpose, we interpreted the general type two membership function as a set of infinite horizontal layers of uncertainty footprints, and then a closed formula for defuzzification is calculated using the uncertainty avoidance method for the triangular shape of secondary membership degree forms. One of the abilities that is expected from Fuzzy logic, particularly General Type-2 Fuzzy Systems (GT2FS), is their effective role in noise handling. In this article, we emphasize providing a method that can be used more consciously to handle noise. To demonstrate the effectiveness of the proposed multilayer approach, it has been applied to image segmentation, a critical aspect of image processing often hindered by challenges like noise, low contrast, and blurred features. The proposed multilayer type-2 fuzzy method based on the uncertainty avoider method has shown very good performance in image segmentation, especially in the case of high-noise images, compared to type one Fuzzy C-Means (FCM) and type-2 FCM methods as well as hybrid methods presented recently in the literature.