###### Abstract
MCDM methods have also been adapted for formulating and solving image processing problems, including segmentation. The authors of [11] investigated the application of evaluation based on the distance from average solution (EDAS) method within the context of image segmentation. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was incorporated as a component within the model for image segmentation fusion [12]. A novel edge detection method for color images was suggested, utilizing MCDM combined with evidential reasoning and widely used edge detection operators [13]. The wide variety of evaluation criteria that can be used for image segmentation quality, including both objective metrics and subjective evaluations, makes MCDM methods well-suited for the problem of selecting the most appropriate segmentation method. Since MCDM methods can handle both quantitative and qualitative criteria, this offers a way to blend objective measures with subjective human judgments. The popular objective quality evaluation metrics assess the segmented image according to one feature, while with the help of MCDM methodology using a set of selected visual features for which we do not always have objective metrics, we can have a more sensitive qualitative evaluation. Image content varies widely, and MCDM methods can provide a flexible framework to adapt to different data types, allowing users to incorporate various image-specific criteria without constraints. In parallel, using fuzzy set theory in MCDM enables the representation and handling of uncertainty and imprecision within these criteria, such as subjectivity in human perception or lack of ground truth data. Intuitionistic fuzzy set (IFS) theory [14] is an extension of the traditional fuzzy set theory, which was introduced by [PERSON] in the 1960s. While fuzzy sets represent uncertainty and vagueness in a mathematical framework, intuitionistic fuzzy sets incorporate an additional dimension called \"non-membership\". IFS theory recognizes that in many real-world scenarios, uncertainty is not only characterized by degrees of membership but also by degrees of non-membership. This recognition allows for a more comprehensive and nuanced representation of uncertainty, which is a common problem in image segmentation. IFS theory has applications in various domains, including image segmentation. For example, traditional FCM faces challenges when segmenting infrared ship targets due to interference from non-ship objects and variations in intensity. The intuitionistic fuzzy C-means (IFCM) method [2] was designed specifically for infrared ship segmentation. The authors of [15] introduced the Bias Corrected Intuitionistic Fuzzy Clustering with Spatial Noise Inhibition (BCIFCMSNI) method based on IFS theory. This method is designed for image segmentation to address issues related to bias fields and noise artifacts in MRI images. The BCIFCMSNI method utilizes the Euclidean intuitionistic fuzzy distance measure to cluster the IFS. This approach uses the principles of IFS theory to effectively manage the inherent vagueness and uncertainty found in MRI images. MCDM methods can be extended to incorporate various other fuzzy sets. Pythagorean neutrosophic sets (PNSs) [16; 17; 18] are an extension of neutrosophic sets, which themselves are an extension of fuzzy sets. PNSs assign three values to each element: truth-membership, indeterminacy-membership, and falsity-membership, with the constraint that the square of the truth-membership, indeterminacy-membership, and falsity-membership sum up to one, satisfying the Pythagorean theorem. Another newer concept is the Pythagorean picture fuzzy set (PPFS), which combines the picture fuzzy set with [PERSON]'s Pythagorean fuzzy set [19] and is applied to developing the Pythagorean fuzzy C-means (PFCM) algorithm [20]. The ability to assess and compare the segmentation methods is equally important.