Abstract
The diagnosis of urinary tract calculi begins with a focused history of calculi, evolution of its symptoms and the duration. Computer assisted approaches for analyzing the images have increased since the manual interpretation of the image is a time consuming process and susceptible to human errors. The proposed system develops a multi-scale wavelet based Bayesian speckle suppression method for ultrasound kidney images. The logarithmic transform of the original image is analyzed into the multi-scale wavelet domain. The subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions. Bayesian estimators are designed to exploits these statistics. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted Kidney segmentation and disease diagnosis applications. Automatic Kidney segmentation from US images, however, remains a challenge due to speckle noise and various other artifacts inherent to US. This paper, design intensity invariant local image phase features, obtained using improved Gabor filter banks, for extracting edge texture features that occur at core and intermediate layer interfaces. The proposed model does the extension of phase symmetry features to modified gabor mode and their use in automatic extraction of kidney edge texture features from US normal and diseased patient images. The system functionality is proved qualitatively and quantitatively through experimentation for synthetic and real data sets. The localization feature value threshold is evaluated with the training samples of US images. The speckle noise error ratio with respect to the standard US image are compared and experimented.
Highlights
Ultrasound (US) imaging is non-ionizing, fast, portable, inexpensive and capable of real time imaging, but US images typically contain significant speckle and other artifacts which complicate image interpretation and automatic processing was shown in Daanen, V., Tonetti, J., Troccaz, J(2004)
If anatomical structures of interest could be visualized and localized with sufficient accuracy and clarity, US may become a strong practical alternative imaging modality for selected applications in Kidney disease diagnosis, for computer-assisted applications where the image can be processed to provide quantitative information on the kidney structures
We study different definitions of texture that applied to US kidney images
Summary
Ultrasound (US) imaging is non-ionizing, fast, portable, inexpensive and capable of real time imaging, but US images typically contain significant speckle and other artifacts which complicate image interpretation and automatic processing was shown in Daanen, V., Tonetti, J., Troccaz, J(2004). Imaging speckle is a phenomenon that occurs when a coherent source and a non-coherent detector are used to interrogate a medium, which is rough on the scale of the wavelength. Speckle occurs especially in images of the liver and kidney whose underlying structures are too small to be resolved by large wavelength ultrasound. Alpha-stable distributions, a family of heavy-tailed densities, are sufficiently flexible and rich to appropriately model wavelet coefficients of images in coding applications. Present a novel speckle suppression method for medical ultrasound images. The proposed processor consists of two major modules: a) A sub band representation function that utilizes the wavelet transform and
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