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To improve the precision and anti-noise performance of infrared image segmentation, an improved and interactive Otsu image segmentation algorithm is proposed. The characteristics of entropy and class variance of images are used to improve the threshold discriminant function of the classical Otsu algorithm that obtains the optimal threshold. The optimal threshold can segment the target from the background and has a good edge retention effect, which improves the segmentation accuracy of Otsu algorithm. Meanwhile, for the feature of single target in infrared image, the idea of interactive rough split is used before segmenting the image. The partial closure of the area containing the target is extracted firstly in the infrared image, and then in the extracted region. The experimental results for the extraction of laser spot target in infrared image show that the improved Otsu segmentation algorithm greatly reduces the impact of the background noise on the segmentation algorithm, also improves the anti-noise performance and segmentation accuracy of the traditional Otsu algorithm and minimizes the computation of the segmentation algorithm, maintaining the target fuzzy edge. The segmentation performance is better than the traditional Otsu segmentation and several related improved Otsu algorithms.