Thyroid disease is a frequent occurrence in clinical practice and the computerized analysis of ultrasonography has been becoming the most prospective tool for thyroid disease automatic diagnosis. However, the accuracy of vision-based diagnostic analysis is often reduced because the
quality of ultrasound image is easily corrupted by the speckle noise. Thus, noise suppression is imperative and significant for the thyroid ultrasonography image preprocessing to increase the reliability of subsequent analysis. In this paper, we propose a novel weighted image averaging method
based on anisotropic diffusion filters combination to remove speckle noise and enhance the details of the image at the same time. The method first denoises the image separately by two filters with different performances. The speckle reducing anisotropic diffusion filter can enhance the details
of the image, and the anisotropic diffusion filter can better suppress the speckle noise in the image. In order to integrate the advantages of the two filters and reduce the mutual interference meanwhile, an adaptive weighted image averaging method is further proposed to combine the pixels
of the two denoised images. The experimental results indicate that the proposed method can achieve promising performance on the template images with various noise levels by considering PSNR and SSIM. What's more, it is not only superior to other methods in automatic segmentation, but also
can obtain better visual effect for thyroid images.
Fuzzy Based Adaptive Anisotropic Diffusion Despeckling Filter
