Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.
Image fusion quality assessment plays a critically important role in the field of medical imaging. To evaluate image fusion quality effectively, a lot of assessment methods have been proposed. Examples include mutual information (MI), root mean square error (RMSE), and universal image quality index (UIQI). These image fusion assessment methods could not reflect the human visual inspection effectively. To address this problem, we have proposed a novel image fusion assessment method which combines the nonsubsampled contourlet transform (NSCT) with the regional mutual information in this paper. In this proposed method, the source medical images are firstly decomposed into different levels by the NSCT. Then the maximum NSCT coefficients of the decomposed directional images at each level are obtained to compute the regional mutual information (RMI). Finally, multi-channel RMI is computed by the weighted sum of the obtained RMI values at the various levels of NSCT. The advantage of the proposed method lies in the fact that the NSCT can represent image information using multidirections and multi-scales and therefore it conforms to the multi-channel characteristic of human visual system, leading to its outstanding image assessment performance. The experimental results using CT and MRI images demonstrate that the proposed assessment method outperforms such assessment methods as MI and UIQI based measure in evaluating image fusion quality and it can provide consistent results with human visual assessment.