Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions

• A new function is used to generate an intuitionistic fuzzy set from various fuzzy sets.
• Confusion in selecting appropriate membership function is modeled as an uncertainty.
• A new intuitionistic fuzzy algorithm is developed and applied to find threshold value.
• Comparison results reveal that the new method surpasses seven other existing techniques.

Segmentation is the process of extraction of objects from an image. This paper proposes a new algorithm to construct intuitionistic fuzzy set (IFS) from multiple fuzzy sets as an application to image segmentation. Hesitation degree in IFS is formulated as the degree of ignorance (due to the lack of knowledge) to determine whether the chosen membership function is best for image segmentation. By minimizing entropy of IFS generated from various fuzzy sets, an image is thresholded. Experimental results are provided to show the effectiveness of the proposed method.