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

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR dat

Speckle Noise Reduction in Medical Ultrasound Images

Ultrasound imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. In this paper, we propose a denoising approach which combines logarithmic transformation and a non linear diffusion tensor. Since speckle noise is multiplicative and nonwhite process, the logarithmic transformation is a reasonable choice to convert signaldependent or pure multiplicative noise to an additive one. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image. To illustrate the effective performance of our algorithm, we present some experimental results on synthetically and real echographic images

A Low-Complexity Bayesian Estimation Scheme for Speckle Suppression in Images

Speckle noise reduction is a crucial pre-processing step for a successful interpretation of images corrupted by speckle noise, and thus, it has drawn a great deal of attention of researchers in the image processing community. The Bayesian estimation is a powerful signal estimation technique and has been widely used for speckle noise removal in images. In the Bayesian estimation based despeckling techniques, the choice of suitable signal and noise models and the development of a shrinkage function for estimation of the signal are the major concerns from the standpoint of the accuracy and computational complexity of the estimation. In this thesis, a low-complexity wavelet-based Bayesian estimation technique for despeckling of images is developed. The main idea of the proposed technique is in establishing suitable statistical models for the wavelet coefficients of additively decomposed components, namely, the reflectance image and the signal-dependant noise, of the multiplicative degradation model of the noisy image and then in using these two statistical models to develop a shrinkage function with a low-complexity realization for the estimation of the wavelet coefficients of the noise-free image. A study is undertaken to explore the effectiveness of using a two sided exponential distribution as a prior statistical model for the discrete wavelet transform (DWT) coefficients of the signal-dependant noise. This model, along with the Cauchy distribution, which is known to be a good model for the wavelet coefficients of the reflectance image, is used to develop a minimum mean square error (MMSE) Bayesian estimator for the DWT coefficients of the noise-free image. A low-cost realization of the shrinkage function resulting from the MMSE Bayesian estimation is proposed and its efficacy is verified from the standpoint of accuracy as well as computational cost. The performance of the proposed despeckling scheme is evaluated on both synthetic and real SAR images in terms of the commonly used metrics, and the results are compared to that of some other state-of-the-art despeckling schemes available in the literature. The experimental results demonstrate the validity of the proposed despeckling scheme in providing a significant reduction in the speckle noise at a very low computational cost and simultaneously in preserving the image details

Novel Approach for Diagnosis of Brain Diseases byUsing Mixed Scheme on MRI and CT Images

Now days, multimodal medical image has growing interest in the field of analysis and diagnosis of brain diseases. In order to obtain complementary information from multimodal input images, multimodal image fusion become widely popular. Here fusion of the input multimodal images is done either by Spatial Domain or by Transform Domain method. Limitations of Spatial domain method force us to use transform domain fusion method. Discrete Wavelet Transform is one of the popular real valued wavelet transform method of transform domain fusion, but it has disadvantages like shift sensitivity and lack of phase information. These disadvantages motivate us to use the complex Wavelet Transform. In the present work we prefer New Daubechies Complex Wavelet Transform (DCxWT) Method for multimodal image fusion.shift invariance and availability of phase information properties of DCxWT create an output fused image of greater quality. In this work we apply two separate image fusion rule for approximation and detailed coefficient

DESPECKLEING PROSTATE ULTRASONOGRAMS USING PDE WITH WAVELET

Prostate cancer is the leading cause of death for men, since the cause of the disease is mysterious and its early detection is also monotonous. Ultrasound (US) is the most popular tool to detect the human organ glands and also used to diagnose the prostate cancer. Speckle noise is an inherent nature of ultrasound images, which degrades the image quality. So far, No specific filter is available to suppress the speckle noise in prostate image. In this paper, a novel despeckling method PDE with Wavelet is presented for prostate US images. The enhancement method is evaluated by using standard measures like Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Edge Preservation Index (EPI). Further, the despeckling approaches is also evaluated time and space complexity. From the results, it is observed that the filtering method PDE with Wavelet is superior to PDE in terms of denoising and also preserving the information content