The data acquisition of 3D-Ultrasound includes array scan and mechanical scan, and the later one is more easy to realize. Currently, the traditional probe scanning mode is Front-end scanning. Under the above scanning ...
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The data acquisition of 3D-Ultrasound includes array scan and mechanical scan, and the later one is more easy to realize. Currently, the traditional probe scanning mode is Front-end scanning. Under the above scanning mode, when it scans over the breast, 2D-Ultrasound probe slides through the surface of the patients' bodies, the image will be influenced strongly by the human *** this paper we propose a new scanning mode to solve the above problem: Back-End scan, the back end rotates while the front end contacts the patients' skin without slide. The device designed using dual stepper motors which are under the synchronization control. Experiment results show the effectiveness of the proposed device.
Traditional image matching algorithm based on gray correlation provides accurate results but it is time-consuming because of large amount of calculation. An improved gray correlation based image matching algorithm bas...
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Traditional image matching algorithm based on gray correlation provides accurate results but it is time-consuming because of large amount of calculation. An improved gray correlation based image matching algorithm based on multi-core DSP is proposed to speed up the matching velocity with the acceptable margin of errors. With the development of portable embedded image processer, especially the multi-core DSPs for parallel computing to speed up the process, these image matching algorithms need to be transplanted to these embedded system. Experiments based on CPU in the form of Visual C++6.0 application program and multi-core Digital Signal Processor(DSP) verify the effectiveness of the algorithm, making it applicable to embedded imageprocessing system with a multi-core DSP.
This paper gives an overview of three different geometric active contour models with the focus on the application to carotid plaque detection from cross-sectional ultrasound images of the carotid artery. On one hand, ...
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This paper gives an overview of three different geometric active contour models with the focus on the application to carotid plaque detection from cross-sectional ultrasound images of the carotid artery. On one hand, basic principles of these geometric active contour models are presented. On the other hand, performance of these models are tested on 16 images and compared with the manual delineations.
This paper presents an algorithm to find the shortest path in 3D(three-dimensional) prostate surgery planning. Using a simplified delay pulse coupled neural network(S-DPCNN) model, a shortest path can be drawn automat...
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This paper presents an algorithm to find the shortest path in 3D(three-dimensional) prostate surgery planning. Using a simplified delay pulse coupled neural network(S-DPCNN) model, a shortest path can be drawn automatically from the target position to the puncture point. Compared to the traditional pulse coupled neural network(PCNN), S-DPCNN needs much fewer neurons and therefore decreases complexity of computation. Experiments on computer simulations show the validity of this method.
An unsupervised change detection method based on spectral clustering and difference image methods for multitemporal single-channel single-polarization synthetic aperture radar (SAR) images is proposed. The difference ...
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An unsupervised change detection method based on spectral clustering and difference image methods for multitemporal single-channel single-polarization synthetic aperture radar (SAR) images is proposed. The difference image is generated by integrating the typical difference image method with Non-Local Filter, which exploits both the spatial neighborhood information and gray similarity information, and can well reduce the speckle noises of SAR images. The spectral clustering algorithm is employed to cluster the difference image into two clusters and get the change map. Compared with traditional clustering algorithms, such as A-means, SC can recognize the clusters of unusual shapes and obtain the globally optimal solutions. Experimental results confirm the effectiveness of the proposed techniques.
A three dimensional transrectal ultrasound imaging system has been developed. The proposed system is aimed at monitoring for the prostate and providing three-dimensional ultrasound image for prostate biopsy and therap...
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A three dimensional transrectal ultrasound imaging system has been developed. The proposed system is aimed at monitoring for the prostate and providing three-dimensional ultrasound image for prostate biopsy and therapy. The device contains mechanical device and control circuit for the rotation of the probe. In addition, a two dimensional ultrasound machine for 2D ultrasound imaging is used and the software system for the reconstruction of three-dimensional ultrasound image based on PC is developed. Experiments are performed in phantom to validate our prototype. The 3D scan of the agar phantom produces minimal geometric distortion. In the future, our system will be tested on patients.
In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach...
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In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach the generalized Gaussian distribution. We use the principal component analysis (PCA) for every similarity window of shearlet coefficients. Then we use Generalized Gaussian model of non-local means method to handle the shearlet coefficients. Finally, we reconstruct image with the new shearlet coefficients to obtain the result. Numerical results show that our algorithm competes favorably with nonlocal means algorithms in the case of high noise.
This paper presents a wavelet-based multiscale products scheme for synthetic aperture radar (SAR) image despeckling. A compactly supported quadratic spline function that approximates the first derivative of Gaussian ...
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This paper presents a wavelet-based multiscale products scheme for synthetic aperture radar (SAR) image despeckling. A compactly supported quadratic spline function that approximates the first derivative of Gaussian is employed in the scheme to decompose log-transformed SAR images. The multiplied results of the decomposed coefficients of adjacent scales consist of multiscale products. The multiscale products can sharp the important structures while weakening noise. A spatially selective neighborhood technique by iteratively selecting neighborhood system in the multiscale products is introduced in searching the important structure information. The influence of the spatial information is imposed on the multiscale products, instead of on the wavelet coefficients, which improves the capability of identifying important features. Experiments show that the proposed scheme is better in SAR image despeckling and preserving edges and detail information than other waveletbased multiscale products methods.
For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) mo...
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For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) model is proposed based on image feature for images denoising. The PDE model is adaptive within each region according to the details of the image feature to adjust the size of the diffusion coefficient. So it can be disposed the high gradient noise at the same time better to retain the edge information. We also analyze the performance of the PDE model method. Numerical results show that our algorithm competes favorably with state of the-art TV projection methods to eliminate noise and reduce Gibbs-type artifacts.
In this paper, we focus on the distribution of eigenvalues, and based on Gaussian assumption, then we do an analysis of the eigenvalues potential for POL-SAR classification. Generally, we use Gaussian mixture model to...
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In this paper, we focus on the distribution of eigenvalues, and based on Gaussian assumption, then we do an analysis of the eigenvalues potential for POL-SAR classification. Generally, we use Gaussian mixture model to describe the distribution of the eigenvalue and Bayesian classifier to achieve the POL-SAR pixel classification. The method is tested with the NASA/JPL AIRSAR data.
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