Most Cited Articles

We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

我们提出了一种用于心脏计算机断层扫描（CT）容积定量功能分析的自动四腔心分割系统。讨论了两个主题：心脏建模和对未知容积的自动模型拟合。心脏建模是一项非平凡的任务，因为心脏是一个复杂的非刚性器官。模型必须解剖学准确，允许手动编辑，并提供足够的信息来指导自动检测和分割。与以往的工作不同，我们在模型的控制点中显式地表示了重要的标志点（如瓣膜和室间隔尖）。这些控制点可以可靠地检测到，以指导自动模型拟合过程。利用该模型，我们开发了一种高效且鲁棒的方法，用于三维CT容积中的自动心脏腔室分割。我们将分割公式化为一个两步学习问题：解剖结构定位和边界描绘。在这两个步骤中，我们利用了判别模型学习的最新进展。引入了一种新颖的算法——边缘空间学习（MSL）来解决用于定位心腔的9维相似变换搜索问题。在确定心腔的姿态后，我们通过基于学习的边界描绘来估计三维形状。该方法已在文献报道的最大数据集（来自137名患者的323个容积）上进行了广泛测试。据我们所知，我们的系统是最快的，所有四个腔室的自动分割速度为每个容积4.0秒（在双核3.2-GHz处理器上）。

This paper presents a new nonrigid monomodality image registration algorithm based on $B$-splines. The deformation is described by a cubic $B$-spline field and found by minimizing the energy between a reference image and a deformed version of a floating image. To penalize noninvertible transformation, we propose two different constraints on the Jacobian of the transformation and its derivatives. T...

本文提出了一种基于B样条的非刚性单模态图像配准算法。变形由三次B样条场描述，并通过最小化参考图像与浮动图像变形版本之间的能量来求解。为了惩罚不可逆变换，我们提出了两种不同的约束条件，分别作用于变换的雅可比矩阵及其导数。...

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

本文综述了超声分割方法，广义上侧重于医学B型超声图像技术。首先，我们通过临床应用回顾文章，突出已研究的方法和不同临床领域中的验证程度。然后，我们根据先验信息的使用对方法进行分类。最后，我们选出十篇提出了原创思想的论文，这些思想在超声分割问题上显示出特别的临床实用性或潜力。

In the technique known as gridding, the data samples are weighted for sampling density and convolved with a finite kernel, then resampled on a grid preparatory to a fast Fourier transform. The authors compare the artifact introduced into the image for various convolving functions of different sizes, including the Kaiser-Bessel window and the zero-order prolate spheroidal wave function (PSWF). They also show a convolving function that improves upon the PSWF in some circumstances. >

在称为网格化的技术中，数据样本根据采样密度进行加权，并与有限核进行卷积，然后在网格上重新采样，以准备快速傅里叶变换。作者比较了不同大小的各种卷积函数引入图像的伪影，包括Kaiser-Bessel窗口和零阶扁长球面波函数（PSWF）。他们...

A partial-differential equations (PDE)-based system for detecting the boundary of skin lesions in digital clinical skin images is presented. The image is first preprocessed via contrast-enhancement and anisotropic diffusion. If the lesion is covered by hairs, a PDE-based continuous morphological filter that removes them is used as an additional preprocessing step. Following these steps, the skin l...

提出了一种基于偏微分方程（PDE）的系统，用于检测数字临床皮肤图像中皮肤病变的边界。图像首先通过对比度增强和各向异性扩散进行预处理。如果病变被毛发覆盖，则使用基于PDE的连续形态学滤波器将其移除作为额外的预处理步骤。在这些步骤之后，皮肤病变...

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.

血流动力学因素被认为与颅内动脉瘤的进展和破裂有关。当前的研究旨在探究血流动力学特征（如动脉瘤内血流模式的复杂性和稳定性、血流冲击区域的大小和位置）与动脉瘤破裂临床史之间的可能关联。然而，目前尚无可靠的方法在体内测量血流模式。本文介绍了一种基于医学图像对脑动脉瘤血流动力学进行患者特异性建模和表征的高效方法。同时，还呈现了在预期生理条件下多个变量变化对血流动力学特征影响的敏感性分析。该敏感性分析表明，尽管可以观察到速度场的变化，但当平均输入流量、流量分配、黏度模型或网格分辨率改变时，动脉瘤内血流模式的表征并未改变。研究还发现，对计算流场影响最大的变量是血管结构的几何形状。我们得出结论，利用所提出的建模流程，涉及大量脑动脉瘤的临床研究是可行的。

This paper presents the adaptive axial interpolator (AAI), a novel spiral interpolation approach for multislice spiral computed tomography (CT) implemented in a clinical multislice CT scanner, the SOMATOM Volume Zoom (Siemens Medical Systems, Forchheim, Germany). The method works on parallel-beam data generated from the acquired fan-beam data by azimuthal rebinning. Spiral interpolation is perform...

本文介绍了自适应轴向插值器（AAI），这是一种用于多层螺旋计算机断层扫描（CT）的新型螺旋插值方法，已在临床多层螺旋CT扫描仪SOMATOM Volume Zoom（西门子医疗系统，德国福希海姆）中实现。该方法通过对采集的扇形束数据进行方位角重排，生成平行束数据。螺旋插值执行...

Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained...

噪声是低剂量CT采集固有的。我们提出联合训练卷积神经网络（CNN）和对抗性CNN，从低剂量CT图像估计常规剂量CT图像，从而降低噪声。训练生成器CNN，通过体素级损失最小化将低剂量CT图像转换为常规剂量CT图像。同时训练对抗性判别器CNN...

Infrared thermal imaging of the skin has been used for several decades to monitor the temperature distribution of human skin. Abnormalities such as malignancies, inflammation, and infection cause localized increases in temperature which show as hot spots or as asymmetrical patterns in an infrared thermogram. Even though it is nonspecific, infrared thermology is a powerful detector of problems that affect a patient's physiology. While the use of infrared imaging is increasing in many industrial and security applications, it has declined in medicine probably because of the continued reliance on first generation cameras. The transfer of military technology for medical use has prompted this reappraisal of infrared thermology in medicine. Digital infrared cameras have much improved spatial and thermal resolutions, and libraries of image processing routines are available to analyze images captured both statically and dynamically. If thermographs are captured under controlled conditions, they may be interpreted readily to diagnose certain conditions and to monitor the reaction of a patient's physiology to thermal and other stresses. Some of the major areas where infrared thermography is being used successfully are neurology, vascular disorders, rheumatic diseases, tissue viability, oncology (especially breast cancer), dermatological disorders, neonatal, ophthalmology, and surgery.

红外热成像已被用于监测人体皮肤温度分布数十年。恶性肿瘤、炎症和感染等异常情况会导致局部温度升高，在红外热像图中表现为热点或不对称模式。尽管非特异性，红外热学是检测影响患者生理问题的强大工具。虽然红外成像在许多工业和安全应用中的使用正在增加，但在医学中却有所下降，这可能是因为持续依赖第一代相机。军事技术向医疗用途的转移促使了这次对医学红外热学的重新评估。数字红外相机的空间和热分辨率大大提高，并且有图像处理程序库可用于分析静态和动态捕获的图像。如果在受控条件下拍摄热像图，可以很容易地解释它们以诊断某些疾病，并监测患者对外界冷热等刺激的生理反应。红外热成像成功应用的一些主要领域是神经病学、血管疾病、风湿病、组织活力、肿瘤学（尤其是乳腺癌）、皮肤病、新生儿、眼科和外科手术。

In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the re...

在本文中，我们提出了一种卷积神经网络（CNN）回归方法，以解决现有基于强度的2D/3D配准技术的两个主要限制：1）计算速度慢和2）捕捉范围小。与基于优化的方法不同，后者通过迭代优化变换参数来最大化或最小化表示配准质量的标量度量函数……

A general reconstruction algorithm for magnetic resonance imaging (MRI) with gradients having arbitrary time dependence is presented. This method estimates spin density by calculating the weighted correlation of the observed free induction decay signal and the phase modulation function at each point. A theorem which states that this method can be derived from the conditions of linearity and shift ...

中文摘要翻译待生成

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator

本文聚焦于表面的回顾性拓扑校正。我们提出了一种技术，用于精确校正皮层表面的球面拓扑。具体而言，我们构建从原始表面到球面的映射，以将拓扑缺陷检测为最小的非同胚区域。然后通过沿着在贝叶斯框架中选择的一组非分离环打开和密封表面来校正每个缺陷的拓扑。所提出的方法是一个完全自包含的拓扑校正算法，它基于磁共振成像（MRI）强度轮廓和预期的局部曲率来确定几何精确、拓扑正确的解。应用于实际数据时，我们的方法提供了与经过训练的操作员所做的相似的拓扑校正。

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient and has linear complexity with respect to the input. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the complexity. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BraTS, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while significantly reducing parameters and FLOPs by over 71%, compared to the best method in the literature. Our code and models are available at: https://tinyurl.com/2p87x5xn .

中文摘要翻译待生成

Parallel transmission is a very promising candidate technology to mitigate the inevitable radio-frequency (RF) field inhomogeneity in magnetic resonance imaging at ultra-high field. For the first few years, pulse design utilizing this technique was expressed as a least squares problem with crude power regularizations aimed at controlling the specific absorption rate (SAR), hence the patient safety...

平行传输是一项非常有前景的候选技术，旨在减轻超高场磁共振成像中不可避免的射频场不均匀性。在最初几年，利用该技术的脉冲设计被表述为一个最小二乘问题，并采用粗略的功率正则化来控制特定吸收率（SAR），从而确保患者安全……

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple ...

压缩感知磁共振成像(CS-MRI)能够实现快速采集，这对于许多临床应用非常理想。这不仅可以降低扫描成本、减轻患者负担，还可能减少运动伪影和对比剂冲刷效应，从而获得更好的图像质量。与基于并行成像的快速MRI不同，它利用多个...

Trabecular bone (TB) is a complex quasi-random network of interconnected plates and rods. TB constantly remodels to adapt to the stresses to which it is subjected (Wolff's Law). In osteoporosis, this dynamic equilibrium between bone formation and resorption is perturbed, leading to bone loss and structural deterioration. Both bone loss and structural deterioration increase fracture risk. Bone's me...

松质骨是一个由相互连接的板状和杆状结构组成的复杂准随机网络。松质骨不断重塑以适应其所承受的应力（沃尔夫定律）。在骨质疏松症中，骨形成和骨吸收之间的动态平衡被打破，导致骨质流失和结构恶化。骨质流失和结构恶化都会增加骨折风险。骨的力学...

In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed. Extensions of this technique support 3-D and multiecho magnetic resonance imaging (MRI), incorporating higher spatial and spectral dimensions. The procedure overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fi...

中文摘要翻译待生成

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale...

在二维和三维图像中提取管状物体的中心线是许多临床图像分析任务的一部分。一种常用的管状物体中心线提取方法基于强度脊线追踪。本文评估了初始化、噪声和奇异性对强度脊线追踪的影响，并提出了多尺度启发式和最优尺度...

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the dictionary is adapted to the particular image instance thereby favoring better sparsities and consequently much higher undersampling rates. The proposed alternating reconstruction algorithm learns the sparsifying dictionary, and uses it to remove aliasing and noise in one step, and subsequently restores and fills-in the k-space data in the other step. Numerical experiments are conducted on MR images and on real MR data of several anatomies with a variety of sampling schemes. The results demonstrate dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods. These improvements persist over a wide range of practical data signal-to-noise ratios, without any parameter tuning.

压缩感知(CS)利用磁共振(MR)图像的稀疏性，从欠采样的k空间数据中实现精确重建。最近的CS方法采用了分析性稀疏变换，如小波、曲线波和有限差分。在本文中，我们提出了一种新颖的框架，用于自适应学习稀疏变换（字典），并同时从重度欠采样的k空间数据中重建图像。该框架中的稀疏性施加在重叠的图像块上，强调局部结构。此外，字典适应于特定的图像实例，从而有利于更好的稀疏性，进而实现更高的欠采样率。所提出的交替重建算法学习稀疏字典，并在一步中使用它去除混叠和噪声，然后在另一步中恢复并填充k空间数据。在MR图像和多种解剖结构的真实MR数据上进行了数值实验，采用了多种采样方案。结果表明，与之前的CS方法相比，使用所提出的自适应字典，重建误差显著提高了4-18 dB，可接受的欠采样因子翻倍。这些改进在广泛的实际数据信噪比范围内持续存在，无需任何参数调整。

The authors detail the design and implementation of HANDX, a model-based computer vision system used in the domain of medical image processing. Given a digitized hand radiograph, HANDX segments out specific bones and measures particular parameters of the bones, without requiring specific characterization of noise variations in background contrast and anatomical differences which arise from patient...

中文摘要翻译待生成

The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training data sets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3-D lung nodule characteristics by decomposing a 3-D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules' overall appearance, voxel, and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI data set and compared it to the five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.

在胸部CT上准确识别恶性肺结节对于肺癌的早期检测至关重要，这也为患者提供了最佳治愈机会。尽管深度学习方法最近已成功引入计算机视觉问题，但由于缺乏大规模训练数据集，恶性结节的检测仍面临巨大挑战。本文提出了一种基于多视图知识的协作（MV-KBC）深度模型，利用有限的胸部CT数据区分恶性和良性结节。我们的模型通过将3D结节分解为九个固定视图来学习3D肺结节特征。对于每个视图，我们构建一个基于知识的协作（KBC）子模型，其中设计了三种图像块，分别微调三个预训练的ResNet-50网络，以表征结节的整体外观、体素和形状异质性。我们联合使用九个KBC子模型对肺结节进行分类，并在误差反向传播过程中学习自适应加权方案，使MV-KBC模型能够以端到端的方式进行训练。使用惩罚损失函数以减少假阴性率，同时最小化对MV-KBC模型整体性能的影响。我们在基准LIDC-IDRI数据集上测试了该方法，并与五种最先进的分类方法进行了比较。结果表明，MV-KBC模型在肺结节分类中达到了91.60%的准确率，AUC为95.70%。这些结果明显优于现有最先进的方法。

The noise-equivalent count-rate (NEC) performance of a neuro-positron emission tomography (PET) scanner has been determined with and without interplane septa on uniform cylindrical phantoms of differing radii and in human studies to assess the optimum count rate conditions that realize the maximum gain. In the brain, the effective gain in NEC performance for three-dimensions (3-D) ranges from >5 a...

中文摘要翻译待生成

A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

提出了一种用于医学图像弹性配准的新方法，并将其应用于脑部磁共振图像。实验结果表明，该方法在来自不同受试者的图像叠加中具有非常高的准确性。所提出的算法有两个主要创新点。首先，它使用属性向量，即一组定义在图像中每个体素上并从组织图中计算得到的几何矩不变量（GMIs），来反映不同尺度下的底层解剖结构。属性向量如果足够丰富，可以区分图像的不同部分，这有助于在变形过程中建立解剖对应关系；它还有助于通过减少潜在匹配中的模糊性来减少局部极小值。这是我们的方法（称为用于弹性配准的层次属性匹配机制，HAMMER）与其他通常基于最大化图像相似性的体积变形方法的根本区别。其次，为了避免陷入局部极小值（即次优的差匹配），HAMMER使用连续逼近被优化的能量函数，通过构造具有显著较少局部极小值的低维光滑能量函数来实现。这是通过层次性地选择具有独特属性向量的驱动特征来实现的，从而大幅减少寻找对应关系时的模糊性。多项实验表明，所提出的算法能够精确叠加具有显著解剖差异的个体的图像数据。

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. The authors show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, the authors design a Bayesian estimator that exploits these statistics. They use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, the authors compare their technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and they quantify the achieved performance improvement.

提出了一种用于医学超声图像斑点抑制的新方法。首先，将原始图像的对数变换分析到多尺度小波域。作者表明，超声图像的子带分解具有显著的非高斯统计特性，这些特性最好由诸如α稳定分布等重尾分布族来描述。然后，作者设计了一个利用这些统计特性的贝叶斯估计器。他们使用α稳定模型开发了一个盲噪声去除处理器，对数据执行非线性操作。最后，作者将他们的技术与当前最先进的软阈值和硬阈值方法应用于实际超声医学图像上，并量化了所实现的性能改进。

A new method to correct the barrel distortion of an electronic endoscope image is presented. A correction model assuming circularly symmetric distortion is introduced with the following model parameters: the center of distortion and the coefficients of polynomials representing the distortion correction in the radial direction. If the imaging system is distortion-free, straight lines in the object ...

中文摘要翻译待生成