SEGMENTATION OF LOW-CONTRAST IMAGES IN THE BASIS OF EIGEN SUBSPACES OF TYPE-2 FUZZY MEMBERSHIP FUNCTIONS
DOI:
https://doi.org/10.15588/1607-3274-2025-1-15Keywords:
Image Segmentation, Fuzzy Сlustering, Type-2 Fuzzy Сlustering, orthogonal transformation, singular value decomposition, singular subspacesAbstract
Context. The study addresses the current task of automating a sensitive image segmentation algorithm based on the Type-2 fuzzy clustering method. The research object is low-contrast greyscale images which are outcomes of standard research methods across various fields of human activity.
Objective. The aim of the work is to create a new set of informative features based on the input data, perform sensitive fuzzy
segmentation using a clustering method that employs Type-2 fuzziness, and implement automatic defuzzification in eigen subspace of membership functions.
Method. A method for segmenting low-contrast images is proposed. It consists of the following steps: expanding the feature space of the input data, applying singular value decomposition (SVD) to the extended dataset with subsequent automatic selection of the most significant components, which serve as input for fuzzy clustering using Type-2 fuzzy sets. Clustering is performed using the T2FCM method, which allows the automatic selection of the number of fuzzy clusters based on an initially larger guaranteed number, followed by the merging of close clusters (proximity was defined in the study using a weighted Euclidean distance). After fuzzy clustering, the proposed method integrates its results (fuzzy membership functions) with the input data for clustering, preprocessed using fuzzy transformations. The resulting matrix undergoes another fuzzy transformation, followed by SVD and the automatic selection of the most significant components. A grayscale image is formed based on the weighted sum of these selected components, to which the adaptive histogram equalization method is applied, resulting in the final segmentation output. The proposed segmentation method involves a small number of control parameters: the initial number of fuzzy clusters, the error of the T2FCM method, the maximum number of iterations, and the coefficient of applied fuzzy transformations. Adjusting these parameters to the processed images does not require significant effort.
Results. The developed algorithm has been implemented as software, and experiments have been conducted on real images of different physical nature.
Conclusions. The experiments confirmed the efficiency of the proposed algorithm and recommend its practical application for
visual analysis of low-contrast grayscale images. Future research prospects may include analyzing the informative potential of the algorithm when using other types of transformations of fuzzy membership functions and modifying the proposed algorithm for segmenting images of various types.
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