<p>Image segmentation is a process of dividing images into areas of interest and background. Thresholding, a well-known method, is an approach to segmenting images into two or more areas. The calculation of a threshold is possible to formulate as an optimization problem and the process of segmentation is dividing the pixels below and above the threshold. Quantum approximate optimization algorithm, a quantum circuit-based optimization, can be used for finding the threshold value. After obtaining the threshold, the pixels are segmented into groups through the search of gray values. Grover’s algorithm, a popular search algorithm, searches the gray values for the segmentation. In this paper, an image segmentation algorithm is proposed using a quantum approximate optimization algorithm for threshold calculation along with the Grover’s algorithm. The proposed algorithm was tested on commonly used test images, and compared with different classical algorithms.</p>