During the process of fine segmentation of medical images, although a single threshold can improve the efficiency of processing, there will be the problem of fuzzy features and non-convergence of threshold in denoising of details such as contour extraction. To extract contour information of medical images, a method based on multi-threshold optimization is proposed. This paper analyzes the influence of contour wave transformation on gray correlation degree and noise intensity of different medical images and improves the Bayesian threshold. The middle threshold function was improved by correlation characteristics of contour wave coefficients, and contour features of medical images were constrained by multiple thresholds. Based on the above, the dimension of the medical image was reduced by the wavelet multi-resolution analysis method, and the corresponding threshold search space was obtained. A genetic algorithm was used to find the best quasi threshold in the search space. Through this value, the attribute histogram of the medical image was established, the best feature extraction threshold of the medical image was obtained by the golden section method, and contour feature information of the medical image was extracted. The experimental results show that the proposed method can achieve the fast extraction of the contour feature information of running image, get an ideal feature extraction effect, and has high efficiency of feature extraction.

在医学图像的精细分割过程中，尽管单个阈值可以提高处理效率，但是在轮廓提取等细节去噪中仍存在模糊特征和阈值不收敛的问题。为了提取医学图像的轮廓信息，提出了一种基于多阈值优化的方法。分析了轮廓波变换对不同医学图像灰度相关度和噪声强度的影响，提高了贝叶斯阈值。轮廓波系数的相关特性改善了中间阈值函数，而医学图像的轮廓特征则受到多个阈值的约束。基于以上所述，通过小波多分辨率分析方法减小了医学图像的尺寸，并获得相应的阈值搜索空间。使用遗传算法在搜索空间中找到最佳准阈值。通过该值，建立医学图像的属性直方图，并通过黄金分割方法获得医学图像的最佳特征提取阈值，并提取医学图像的轮廓特征信息。实验结果表明，该方法可以快速提取运行图像的轮廓特征信息，达到理想的特征提取效果，具有较高的特征提取效率。通过黄金分割法获得医学图像的最佳特征提取阈值，并提取医学图像的轮廓特征信息。实验结果表明，该方法可以快速提取运行图像的轮廓特征信息，达到理想的特征提取效果，具有较高的特征提取效率。通过黄金分割法获得医学图像的最佳特征提取阈值，并提取医学图像的轮廓特征信息。实验结果表明，该方法可以快速提取运行图像的轮廓特征信息，达到理想的特征提取效果，具有较高的特征提取效率。