186 research outputs found
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
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
Evaluation of Digital Speckle Filters for Ultrasound Images
Ultrasound (US) images are inherently corrupted by speckle noise causing inaccuracy of medical diagnosis using this technique. Hence, numerous despeckling filters are used to denoise US images. However most of the despeckling techniques cause blurring to the US images. In this work, four filters namely Lee, Wavelet Linear Minimum Mean Square Error (LMMSE), Speckle-reduction Anisotropic Diffusion (SRAD) and Non-local-means (NLM) filters are evaluated in terms of their ability in noise removal and capability to preserve the image contrast. This is done through calculating four performance metrics Peak Signal to Noise Ratio (PSNR), Ultrasound Despeckling Assessment Index (USDSAI), Normalized Variance and Mean Preservation. The experiments were conducted on three different types of images which is simulated noise images, computer generated image and real US images. The evaluation in terms of PSNR, USDSAI, Normalized Variance and Mean Preservation shows that NLM filter is the best filter in all scenarios considering both speckle noise suppression and image restoration however with quite slow processing time. It may not be the best option of filter if speed is the priority during the image processing. Wavelet LMMSE filter is the next best performing filter after NLM filter with faster speed
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