High-resolution venography of the brain using magnetic resonance imaging

The purpose of this study was to evaluate a non-flow related magnetic resonance imaging method to visualize small veins independent of arteries in the human brain. A long TE, high-resolution 3D gradient echo MR acquisition was used to highlight venous information. The method is based on the paramagnetic property of deoxyhemoglobin and the resulting phase difference between veins and brain parenchyma at long echo times. The MR magnitude images were masked with a phase mask filter to enhance small structure visibility.. Venous information down to sub-pixel vessel diameters of several hundred microns is visible. Venous data are displayed in an angiographic manner using a minimum intensity projection algorithm. Both superficial veins and deep white matter veins are visible. The method has been successfully applied in volunteers. Preliminary results in patients with cerebral arteriovenous malformations indicate its potential in clinical applications. The proposed method is easy to implement and does not require administration of a contrast agent or application of specially designed rf pulses to highlight the veins. Rather it exploits the intrinsic magnetic properties (BOLD-effect) and the prolonged T ^_2* of venous blood. The method may be of diagnostic potential in the assessment of arteriovenous malformations or other vascular venous lesions. © 1998 Elsevier Science B.V. All rights reserved.     

Low-field magnetic resonance imaging of the pelvis in patients with anal dynamic graciloplasty: initial experience

The aim of this study was to determine whether low-field magnetic resonance (MR) imaging can safely and accurately depict inflammatory changes in patients with anal dynamic graciloplasty, in whom high-field MR imaging is contraindicated and ultrasonography and computed tomography are inadequate. A 0.2-T field-strength MR examination was performed in six patients with anal dynamic graciloplasty malfunction in whom reoperation was contemplated. The following sequences were applied:T ₂-weighted turbo spinecho with fat saturation,T ₁-weighted conventional spin-echo, and contrastenhancedT ₁-weighted conventional spin-echo with fat saturation. Results indicated that none of the patients experienced relevant discomfort, pacemaker malfunction, or electrode dislocation with low-field MR imaging. Inflammatory pelvic changes were visualized in four patients and atrophy of the transposed gracilis muscle in another. Surgery was thus avoided in the four, who underwent conservative treatment for their pelvic inflammation. It was concluded that these prelininary results demonstrate the feasibility of MR imaging with a low field strength in patients with anal dynamic graciloplasty. In such patients, in whom diagnostic imaging had been problematic, the potential for safe and accurate visualization will be a boon to treatment planning.     

Retrospective analysis for evaluation of the value of contrast-enhanced MRI in patients treated with breast conservative therapy

Purpose The aim of this study was to assess the value of contrast-enhanced (c.-e.) MRI in the follow-up of patients with conservatively treated breast cancer since detection and exclusion of malignancy may interfere significantly with posttherapeutic changes within the treated breast. Material and methods A total of 207 patients with a history of limited surgery and radiation therapy underwent MR imaging, 40 patients were examined 0–12 months and 167 patients were examined later than 12 months after radiotherapy. Suspicious or indeterminate findings were suggested by clinical examination or conventional imaging in 80 studies. In 127 women, MRI was performed within breast tissue that was difficult to assess due to scarring or dense breast tissue. Results Recurrent carcinoma was confirmed in 27 patients by surgical biopsy. All 27 carcinomas, except for one with a slow signal increase, demonstrated early rise of signal intensity on dynamic T1-weighted contrast enhanced images. During the first year after therapy, the diagnostic accuracy could not be improved by additional use of c.-e. MRI. Differentiation between posttherapeutic changes and recurrent carcinoma was frequently not possible because of strong and sometimes early and ill-circumscribed enhancement. Later than 12 months after therapy enhancement decreased significantly, thus the false positive calls could be reduced from 49 (conventional imaging) to 12 (conventional imaging plus MRI). A total of 12 of 26 recurrences and multifocality in 4/5 cases were diagnosed by MR imaging alone at this time interval. Conclusion In the first year after therapy, c.-e. MRI is only indicated in selected cases. The results later than 12 months emphasize that c.-e. MRI may contribute significant additional information. It allows better distinction of posttherapeutic fibrosis from recurrent carcinoma and proved to be able to detect recurrent disease more sensitive and at an earlier stage.     

基于视频图像的车牌定位预处理算法

车辆图像的预处理效果的好坏直接影响着车牌定位的准确率和运算速度;为了去除复杂背景区域的影响,消除图像的噪声,提出了一种针对视频序列改进的预处理流程;基于混合高斯背景建模结合小波包多尺度去噪的思想,去除大量的背景信息,缩小了车牌定位的范围再继续后面的预处理工作;上述方法得到的图片,噪声干扰降低且含有较少的不规则连通区域,分别以文献[6]和文献[7]提供的车牌定位方法为例进行测试,效果良好。     

基于MATLAB的复合数字水印认证系统的设计与应用

为实现多媒体图像的知识产权保护,进行有效的防篡改认证,设计了复合数字水印认证系统。利用该系统可以对单个或批量的多媒体图像嵌入鲁棒性水印及脆弱性水印。仿真实验证明,多媒体数字图像嵌入复合数字水印后和原始图像几呼一样,水印隐蔽效果好;当图像被篡改后,用该系统进行水印认证,可以清晰看到版权标志信息是否被篡改以及定位显示篡改的内容。     

改进的Mean Shift滤波在砂砾岩图像分割中的应用

砂砾岩图像成分颜色多样、色彩不均,要有效地进行分割,提取砾岩颗粒,图像预处理就显得尤为重要。MeanShift滤波相比传统的滤波算法具有更好的平滑特性,能很好地消除噪声和色彩不均对后续分割造成的影响,但运算速度较慢。针对该问题,提出利用高斯金字塔和像素点赋值方法改进Mean Shift滤波算法速度。采用改进后的Mean Shift滤波算法对砂砾岩图像进行预处理,然后进行分割,提取粒岩,取得了较好的效果。     

New Riemannian techniques for directional and tensorial image data

This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton's method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona-Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo's structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images.     

A systematic method for efficient computation of full and subsets Zernike moments

A new method is proposed for fast and accurate computation of Zernike moments. This method presents a novel formula for computing exact Zernike moments by using exact complex moments where the exact values of complex moments are computed by mathematical integration of the monomials over digital image pixels. The proposed method is applicable to compute the full set of Zernike moments as well as the subsets of individual order, repetition and an individual moment. A comparison with other conventional methods is performed. The results show the superiority of the proposed method.     

Segmentation and classification of hyperspectral images using watershed transformation

Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral-spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral-spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures.