脊波变换在眼底图像中的去噪效果评价

Effect Evaluation of De-noising for Fundus Images Based on Ridgelet Transform

针对实际的眼底图像中含有噪声,严重影响对病灶的诊断,根据其先验知识,本文采用四种多尺度几何变换技术对其进行去噪处理,即脊波、加维纳滤波的脊波、小波和轮廓波算法;并给出基于高斯波形提取的LMLSD(局部均值和局部标准差)算法,估算处理后图像的SNR(信噪比),对这四种去噪算法的处理效果进行客观的量化评价。实验结果表明,图像经加维纳滤波的脊波去噪算法处理后,图像最为清晰,SNR提高最为明显,较原始图像提高约5.04倍,客观量化评价结果与主观视觉感受一致。

Noise in fundus images seriously affects the diagnosing of lesions. On the basis of prior knowledge about fundus images,four multiscale geometric transforms are adopted for de-nosing,which are respectively Ridgelet,Ridgelet combined with Wiener filer,Wavelet,Contourlet. The Local Mean and Local Standard Deviation algorithm is given based on Gaussian wave extraction,which is used to estimate Signal-to-Noise Ratio（SNR） of the processed images and give an objective quantitative evaluation to treatment effect of the above de-noising algorithms. The results show that the image processed by Ridgelet combined with Wiener filer is the most clearest,and its SNR improves the most obviously,about 5.04 times compared with the original image. The results of objective quantitative evaluation are in accordance with subjective visual feeling.