角膜神经显微图像的自适应渐晕校正

Adaptive vignetting correction of corneal nerve microscopy images

通过拼接角膜神经图像可以减小显微图像视场小的影响。由于显微图像存在渐晕效果,拼接图像会在拼接处产生伪影,影响医生诊断。为解决拼接图像的渐晕伪影问题,提出了一种通过非线性多项式函数建模进行图像渐晕校正的方法。首先,对单张角膜神经图像建立渐晕模型,设置符合渐晕物理性质的约束条件,利用L-M优化算法对渐晕模型参数进行迭代优化。在每次迭代优化过程中,计算对数信息熵,对当前渐晕模型的校正效果进行判断,防止图像过度校正。迭代优化结束后,将渐晕模型反向补偿原图像,完成渐晕校正处理。通过对比校正前后的拼接图像,校正后图像在拼接处无明显的渐晕伪影。实验测试5组不同患者的图像,校正后图像MSE、PSNR、SSIM评估指标平均值分别达到0.0042、72.2251 dB、0.9600,具有最佳的校正效果。本文算法的校正效果明显优于其他同类算法的校正效果。该方法能够有效地对角膜图像渐晕效果进行校正,无须提前设置固定的相机和环境亮度参数。校正后图像拼接效果良好,可获得更加准确、清晰、视野范围大的角膜神经拼接图像。

The effect of a small field of view of microscopic images can be improved by stitching corneal nerve images.Owing to the vignetting effect of microscopic images,the stitched images can produce artifacts at the stitch site,affecting the diagnosis.To solve the problem of vignetting artifacts in stitched images,this study presents a method for correcting image vignetting by using nonlinear polynomial function modeling.First,a vignetting model is established for a single corneal neural image,constraints consistent with the physical properties of the vignetting are set,and the parameters of the vignetting model are iteratively optimized using the Levenberg–Marquardt optimization algorithm.During each optimization iteration,the logarithmic information entropy is calculated to determine the correction effect of the current vignetting model and prevent overcorrection of the image.At the end of the iterative optimization,the vignetting model is reversed to compensate for the original image and complete the vignetting correction process.A comparison of the stitched images before and after correction reveals that the corrected images have no obvious vignetting artifacts at the stitch site.Experiments on the images of five patient groups show that the mean values of the mean squared error,peak signal-to-noise ratio,and structural similarity evaluation indices of the corrected images reach 0.0042,72.2251,and 0.9600,respectively,with the best correction effect.The correction effect of the proposed algorithm is significantly better than that of other similar algorithms.The proposed method can effectively correct corneal image vignetting effects without cameras or environmental brightness parameters being fixed in advance.The corrected-image stitching effect is good;corneal-nerve stitching images that are more accurate and clearer with a larger field of view can be obtained.