## 2 Related Works  
Segmentation quality assessment and selection of an optimal segmentation algorithm are often challenging, especially when dealing with noisy or complex [21; 22] image content. The MCDM method can consider multiple, sometimes conflicting, criteria to address these challenges to make more informed decisions. Beyond its effectiveness, MCDM brings versatility to image segmentation quality evaluation, enabling the integration of subjective human preferences and adaptation to diverse image characteristics. This ability to handle multiple criteria makes MCDM useful for image segmentation problems. In every segmentation situation, three important and related problems must be solved: (1) selecting an appropriate segmentation algorithm for the data set, (2) determining the optimal number of clusters, and (3) assessing the segmentation quality (typically using quality evaluation metrics [23]). This article addresses the first and third issues. However, the segmentation quality is related to the ranks (the first rank is the best segmentation, etc.) determined by the decision-making method. Several methods and strategies can be employed for selecting the most optimal segmentation algorithm. The most common is comparative analysis, which is conducting practical tests by applying various segmentation algorithms to the specific image content and evaluating their performance using quality metrics [24]. The quality of the active contour model based on local Kullback-Leibler divergence for fast image segmentation [25] was validated using popular DSC (Dice Similarity Coefficient) and IoU (Intersection over Union) external measures. Another active contour model based on the retina and pre-fitting reflectance for fast image segmentation [26] used running time, number of iterations, and IoU measures for comparative tests. The full-reference objective metric developed by the authors of [27] was validated by comparing its performance with other state-of-the-art objective measures. However, consulting experts from a particular field may become indispensable when dealing with uncommon image content, especially when ground truth is not available. Content analysis [28] deals with understanding the image content that can guide the choice of the algorithm. On the other hand, content analysis might involve some level of subjectivity, especially when it comes to defining what features are important. However, using consensus from experts or statistical measures can mitigate this issue. Machine learning-based methodologies [29] involve training models, including deep neural networks, that could predict the optimal segmentation algorithm based on image content statistics. Features extracted from the image, such as texture, color, and shape, can be used as input for the model. The authors of [30] explored the hypothesis of using meta-learning (a subfield of machine learning and artificial intelligence) to select the most suitable image segmentation algorithm. One of the shortcomings of training deep learning networks is the demand for substantial allocation of computational resources [31]. Other techniques are needed when training a model is impractical or the dataset is too small. The authors of [32] propose a model for decision-making support for the evaluation of clustering algorithms (DMSECAs) to estimate the performance of clustering algorithms in selecting the most satisfactory clustering algorithm according to the decision preferences. Research findings indicate that the DMSECAs model, following the principles of the 80-20 rule, can produce a list of algorithm priorities and an optimal ranking scheme that best aligns with the decision preferences of all participants engaged in a decision-making scenario. The viability and efficiency of the presented model are demonstrated and confirmed through a statistical analysis of rankings using 20 UCI datasets.