一种扫描图像几何畸变的数字校正方法

由于胶订书籍不能完全平铺于扫描仪的玻璃板上，从而使得扫描图像产生非线性几何畸变，为此，提出一种对此类畸变图像进行横向畸变数字校正的方法，并详细介绍了实现该数字校正的算法。实验结果表明，此算法能够大大减小横边误差，使畸变图像得到很好的复原。     

Quantification of thyroid volume using 3-D ultrasound imaging

Ultrasound (US) is among the most popular diagnostic techniques today. It is non-invasive, fast, comparably cheap, and does not require ionizing radiation. US is commonly used to examine the size, and structure of the thyroid gland. In clinical routine, thyroid imaging is usually performed by means of 2-D US. Conventional approaches for measuring the volume of the thyroid gland or its nodules may therefore be inaccurate due to the lack of 3-D information. This work reports a semi-automatic segmentation approach for the classification, and analysis of the thyroid gland based on 3-D US data. The images are scanned in 3-D, pre-processed, and segmented. Several pre-processing methods, and an extension of a commonly used geodesic active contour level set formulation are discussed in detail. The results obtained by this approach are compared to manual interactive segmentations by a medical expert in five representative patients. Our work proposes a novel framework for the volumetric quantification of thyroid gland lobes, which may also be expanded to other parenchymatous organs.     

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.     

3-D SPECT simulations of a complex 3-D mathematical brain model: effects of 3-D geometric detector response, attenuation, scatter, and statistical noise

The quantitative imaging characteristics of ultrahigh-resolution parallel-hole SPECT, including 3-D geometric detector response, attenuation, scatter, and statistical noise, were investigated by simulations based on a complex digitized 3-D brain model of the gray and white matter distributions. The projection data resulting from a uniform distribution of gray and white matter radioactivity, in a ratio of 5:1, were simulated. The results demonstrate significant qualitative and quantitative artifacts in reconstructed human brain images. In the absence of attenuation, scatter, and noise, artifactual variation caused inaccuracies in regional radioactivity quantification. Inclusion of attenuation scatter, and noise in the simulation caused additional artifacts, and resulted in reconstructed images which qualitatively and quantitatively corresponded very closely to reconstructed images of the actual 3-D brain phantom which was constructed from the same set of data as the mathematical 3-D brain model. It is concluded that the major degrading factor in SPECT neuroimaging is the 3-D geometric detector response function.     

The magnetic-resonance force microscope: a new tool for high-resolution, 3-D, subsurface scanned probe imaging

The magnetic-resonance force microscope (MRFM) is a novel scanned probe instrument which combines the three-dimensional (3-D) imaging capabilities of magnetic-resonance imaging with the high sensitivity and resolution of atomic-force microscopy. It will enable nondestructive, chemical-specific, high-resolution microscopic studies and imaging of subsurface properties of a broad range of materials. The MRFM has demonstrated its utility for study of microscopic ferromagnets, and it will enable microscopic understanding of the nonequilibrium spin polarization resulting from spin injection. Microscopic MRFM studies will provide unprecedented insight into the physics of magnetic and spin-based materials. We will describe the principles and the state-of-the-art in magnetic-resonance force microscopy, discuss existing cryogenic MRFM instruments incorporating high-Q, single-crystal microresonators with integral submicrometer probe magnets, and indicate future directions for enhancing MRFM instrument capabilities.     

The effect of image distortion on 3-D reconstruction of coronary bypass grafts from angiographic views

Three-dimensional (3-D) reconstructions of coronary bypass grafts performed from X-ray angiographic images may become increasingly important for the investigation of damaging mechanical stresses imposed to these vessels by the cyclic movement of the heart. Contrary to what we had experienced with coronary arteries, appreciable reconstruction artifacts frequently occur with grafts. In order to verify the hypothesis that those are caused by distortions present in the angiographic images (acquired with image intensifiers), we have implemented a grid correction technique in our 3-D reconstruction method and studied its efficiency with phantom experiments. In this article, the nature of the encountered artifacts and the way in which the dewarping correction eliminates them are illustrated by a phantom experiment and by the reconstruction of a real coronary bypass vein graft.     

Pattern tracking and 3-D motion reconstruction of a rigid body from a 2-D image sequence

This paper presents an integrated method to identify an object pattern from an image, and track its movement over a sequence of images. The sequence of images comes from a single perspective video source, which is capturing data from a precalibrated scene. This information is used to reconstruct the scene in three-dimension (3-D) within a virtual environment where a user can interact and manipulate the system. The steps that are performed include the following: i) Identify an object pattern from a two-dimensional perspective video source. The user outlines the region of interest (ROI) in the initial frame; the procedure builds a refined mask of the dominant object within the ROI using the morphological watershed algorithm. ii) The object pattern is tracked between frames using object matching within the mask provided by the previous and next frame, computing the motion parameters. iii) The identified object pattern is matched with a library of shapes to identify a corresponding 3-D object. iv) A virtual environment is created to reconstruct the scene in 3-D using the 3-D object and the motion parameters. This method can be applied to real-life application problems, such as traffic management and material flow congestion analysis.     

Realizations of fast 2-D/3-D image filtering and enhancement

This paper proposes a novel algorithm for multidimensional image enhancement based on a fuzzy domain enhancement method, and an implementation of a recursive and separable low-pass filter. Considering a smoothed image as a fuzzy data set, each pixel in an image is processed independently, using fuzzy domain transformation and enhancement of both the dynamic range and the local gray level variations. The algorithm has the advantages of being fast and adaptive, so it can be used in real-time image processing applications and for multidimensional data with low computational cost. It also has the ability to reduce noise and unwanted background that may affect the visualization quality of two-dimensional (2-D)/three-dimensional (3-D) data. Examples for the applications of the algorithm are given for mammograms, ultrasound 3-D images, and photographic images.     

A "twisting and bending" model-based nonrigid image registration technique for 3-D ultrasound carotid images

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a “twisting and bending” model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. We calculated the mean registration error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our “twisting and bending” model-based nonrigid registration algorithm. We achieved an average registration error of $0.80 pm 0.26$ mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.      

Incorporating a measure of local scale in voxel-based 3-D image registration

We present a new class of approaches for rigid-body registration and their evaluation in studying multiple sclerosis (MS) via multiprotocol magnetic resonance imaging (MRI). Three pairs of rigid-body registration algorithms were implemented, using cross-correlation and mutual information (MI), operating on original gray-level images, and utilizing the intermediate images resulting from our new scale-based method. In the scale image, every voxel has the local "scale" value assigned to it, defined as the radius of the largest ball centered at the voxel with homogeneous intensities. Three-dimensional image data of the head were acquired from ten MS patients for each of six MRI protocols. Images in some of the protocols were acquired in registration. The registered pairs were used as ground truth. Accuracy and consistency of the six registration methods were measured within and between protocols for known amounts of misregistrations. Our analysis indicates that there is no "best" method. For medium misregistration, the method using MI, for small add large misregistration the method using normalized cross-correlation performs best. For high-resolution data the correlation method and for low-resolution data the MI method, both using the original gray-level images, are the most consistent. We have previously demonstrated the use of local scale information in fuzzy connectedness segmentation and image filtering. Scale may also have potential for image registration as suggested by this work.     

Fan filters, the 3-D Radon transform, and image sequence analysis

This paper develops a theory for the application of fan filters to moving objects. In contrast to previous treatments of the subject based on the 3-D Fourier transform, simplicity and insight are achieved by using the 3-D Radon transform. With this point of view, the Radon transform decomposes the image sequence into a set of plane waves that are parameterized by a two-component slowness vector. Fan filtering is equivalent to a multiplication in the Radon transform domain by a slowness response function, followed by an inverse Radon transform. The plane wave representation of a moving object involves only a restricted set of slownesses such that the inner product of the plane wave slowness vector and the moving object velocity vector is equal to one. All of the complexity in the application of fan filters to image sequences results from the velocity-slowness mapping not being one-to-one; therefore, the filter response cannot be independently specified at all velocities. A key contribution of this paper is to elucidate both the power and the limitations of fan filtering in this new application. A potential application of 3-D fan filters is in the detection of moving targets in clutter and noise. For example, an appropriately designed fan filter can reject perfectly all moving objects whose speed, irrespective of heading, is less than a specified cut-off speed, with only minor attenuation of significantly faster objects. A simple geometric construction determines the response of the filter for speeds greater than the cut-off speed.     

Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration

Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction using image registration and voxel based similarity measures. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the normalized mutual information between the images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.     

MEMS振镜扫描共聚焦图像畸变机理分析及校正

在皮肤反射式共聚焦显微成像过程中，针对MEMS振镜二维扫描引起的共聚焦图像畸变，开展了光束偏转理论分析，得出了投影面扫描图像的具体形状表征，理论畸变图像与真实畸变图像一致，明确了畸变机理，提出一种有效的畸变校正算法，实现对图像二维畸变的校正。首先记录原始光栅畸变图像，然后基于Hessian矩阵提取光栅中心线，拾取特征点并设置基准参考线，通过基于最小二乘法的7次多项式插值法标定二维方向像素畸变校正量，采用加权平均法填补间隙像素灰度值，最终实现图像畸变校正。利用网格畸变测试靶实验得出7次多项式插值后的校正决定系数最高、均方根误差值最低，整幅512行图像在7次多项式插值后最优行数占379行，比例为74%，通过残差分析，二维方向上残差最大为4个像素，最小为0个像素，平均为1.15个像素，校正结果较为精确。皮肤在体实时成像实验显示，图像畸变校正后组织结构特征更加真实准确，表明这种校正算法有效可行，有助于皮肤疾病的准确诊断。     

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.     

Efficient implementation of accurate geometric transformations for 2-D and 3-D image processing

This paper proposes the use of a polynomial interpolator structure (based on Horner's scheme) which is efficiently realizable in hardware, for high-quality geometric transformation of two- and three-dimensional images. Polynomial-based interpolators such as cubic B-splines and optimal interpolators of shortest support are shown to be exactly implementable in the Horner structure framework. This structure suggests a hardware/software partition which can lead to efficient implementations for multidimensional interpolation.     

Fast detection and characterization of vessels in very large 3-D data sets using geometrical moments

An improved and very fast algorithm dealing with the extraction of vessels in three-dimensional imaging is described. The approach is based on geometrical moments and a local cylindrical approximation. A robust estimation of vessel and background intensity levels, position, orientation, and diameter of the vessels with adaptive control of key parameters, is provided during vessel tracking. Experimental results are presented for lower limb arteries in multidetector computed tomography scanner.     

Supervised classification in high-dimensional space: geometrical,statistical, and asymptotical properties of multivariate data

The recent development of more sophisticated remote-sensing systems enables the measurement of radiation in many more spectral intervals than was previously possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data greatly enhances the data's information content, but provides a challenge to the current techniques for analyzing such data. Human experience in 3D space tends to mislead our intuition of geometrical and statistical properties in high-dimensional space, properties which must guide our choices in the data analysis process. Using Euclidean and Cartesian geometry, high-dimensional space properties are investigated in this paper, and their implication for high-dimensional data and its analysis is studied in order to illuminate the differences between conventional spaces and hyperdimensional space     

Feasibility of half-data image reconstruction in 3-D reflectivity tomography with a spherical aperture

Reflectivity tomography is an imaging technique that seeks to reconstruct certain acoustic properties of a weakly scattering object. Besides being applicable to pure ultrasound imaging techniques, the reconstruction theory of reflectivity tomography is also pertinent to hybrid imaging techniques such as thermoacoustic tomography. In this work, assuming spherical scanning apertures, redundancies in the three-dimensional (3-D) reflectivity tomography data function are identified and formulated mathematically. These data redundancies are used to demonstrate that knowledge of the measured data function over half of its domain uniquely specifies the 3-D object function. This indicates that, in principle, exact image reconstruction can be performed using a "half-scan" data function, which corresponds to temporally untruncated measurements acquired on a hemi-spherical aperture, or using a "half-time" data function, which corresponds to temporally truncated measurements acquired on the entire spherical aperture. Both of these minimal scanning configurations have important biological imaging applications. An iterative reconstruction method is utilized for reconstruction of a simulated 3-D object from noiseless and noisy half-scan and half-time data functions.     

Finite volume stiffness matrix for solving anisotropic cardiac propagation in 2-D and 3-D unstructured meshes

The finite volume method (FVM) has been shown recently to be an effective method for discretizing the reaction-diffusion equations that govern wavefront propagation in anisotropic cardiac tissue, as it can naturally handle both complex geometries and no flux boundary conditions without the use of ghost nodes. This communication presents an alternative formulation of FVM for triangle and tetrahedral meshes using the concept of dual basis. An algorithm based on this form is given that leads to an efficient computation of the stiffness matrix, facilitating the incorporation of space adaptive schemes and time varying material properties into numerical simulations of cardiac dynamics.     

数字图像处理在三维轮廓形貌测量中的应用

在阐述移相干涉法测量物体三维轮廓基本原理的基础上，对测得的干涉图像进行预处理，并根据条纹扫描波面位相实时检测的数学模型对处理的图像进行计算和高通滤波后，得到了高精表面粗糙度样块的三维轮廓形貌，很好的解决了表面粗糙度三维测量的问题。