Analysis of Different Filters for Image Despeckling : A Review

Digital image acquisition and processing in clinical diagnosis plays a significant part. Medical images at the time of acquisition can be corrupted via noise. Removal of this noise from images is a challenging problem. The presence of signal dependent noise is referred as speckle which degrades the actual quality of an image. Considering, several techniques have been developed focused on speckle noise reduction. The primary purpose of these techniques was to improve visualization of an image followed by preprocessing step for segmentation, feature extraction and registration. The scope of this paper is to provide an overview of despeckling techniques

Analysis and Synthesis Prior Greedy Algorithms for Non-linear Sparse Recovery

In this work we address the problem of recovering sparse solutions to non linear inverse problems. We look at two variants of the basic problem, the synthesis prior problem when the solution is sparse and the analysis prior problem where the solution is cosparse in some linear basis. For the first problem, we propose non linear variants of the Orthogonal Matching Pursuit (OMP) and CoSamp algorithms; for the second problem we propose a non linear variant of the Greedy Analysis Pursuit (GAP) algorithm. We empirically test the success rates of our algorithms on exponential and logarithmic functions. We model speckle denoising as a non linear sparse recovery problem and apply our technique to solve it. Results show that our method outperforms state of the art methods in ultrasound speckle denoising

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images