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Many classical measures partition image according to a single property. Moreover, many schemes suffer from the lack of evaluation of image quality at the global level. This paper proposes a novel image thresholding measure that uses both ultra-fuzzy entropy and ultra-fuzzy similarity properties for grayscale images. The optimal threshold is presented as multi-properties based on type II fuzzy (ultra-fuzzy) sets and comprehensive assessment function. In experiments conducted on various classic images, this algorithm showed notable visual improvement in comparison with common measures.