The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an improved two-threshold quantum segmentation algorithm for NEQR image is proposed, which can segment the complex gray-scale image into a clear ternary image by using fewer qubits and can be scaled to use n thresholds for n + 1 segmentations. In addition, a feasible quantum comparator is designed to distinguish the gray-scale values with two thresholds, and then a scalable quantum circuit is designed to segment the NEQR image. For a 2^(n)*2^(n) image with q gray-scale levels, the quantum cost of our algorithm can be reduced to 60q-6, which is lower than other existing quantum algorithms and does not increase with the image's size increases. The experiment on IBM Q demonstrates that our algorithm can effectively segment the image.

提出了一种改进的NEQR图像的双阈值量子分割算法，利用较少的量子比特将复杂灰度图像分割成清晰的三值图像，并且可以对n个阈值进行n+1次分割，通过设计可行的量子比较器和可扩展的量子电路对NEQR图像进行分割，实验结果表明该算法能够有效地进行图像分割。

一种改进的针对NEQR图像的双阈值量子分割算法