摘要目的 医学图像分割是计算机辅助诊断与治疗的基础技术,对各种自动分割算法性能的验证极为重要；但是临床图像无法直接提供“金标准”,亟需解决导致验证所需的测试数据难以量化评估的问题.方法 使用傅里叶描述子作为描述临床图像中待分割区域轮廓的函数,通过对傅里叶描述子的抽样生成新的轮廓,最后使用纹理匹配技术计算新图像中像素点的灰度值.结果 针对颅内出血的图像,以生成的模拟图像作为测试图像,对多阈值分割和水平集分割算法的精确性和准确性进行了定量的验证.结论 实验表明本方法能够快速地生成逼真的模拟医学图像,对分割算法的验证具有很强的实用性.

abstractsObjective To develop a synthetic medical image generation system which can provide test images for the validation of medical image segmentation algorithms.Methods The synthetic image was created based on the deformation of region of interest (ROI) in original clinical images.First the synthetic foreground boundarywas generated by the resampling of the Fourier descriptors of manually segmented foreground boundary in original image.Then all the ROI pixels were divided into 4 categories and their intensities were calculated by texture matching techniques.Results The intracranial hemorrhage image was selected as the original image,and the generated synthetic images were applied to validate the precision and accuracy of multi-threshold segmentation and level set algorithm.Conclusion The proposed system can rapidly generate synthetic images with realistic appearance of clinical cases and well define ground truth foreground boundary.It has strong practicality for quantitative validation of segmentation algorithms.