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Object tracking is a key enabling technology in the context of computer-assisted medical interventions. Allowing the continuous localization of medical instruments and patient anatomy, it is a prerequisite for providing instrument guidance to subsurface anatomical structures. The only widely used technique that enables real-time tracking of small objects without line-of-sight restrictions is electromagnetic (EM) tracking. While EM tracking has been the subject of many research efforts, clinical applications have been slow to emerge. The aim of this review paper is therefore to provide insight into the future potential and limitations of EM tracking for medical use. We describe the basic working principles of EM tracking systems, list the main sources of error, and summarize the published studies on tracking accuracy, precision and robustness along with the corresponding validation protocols proposed. State-of-the-art approaches to error compensation are also reviewed in depth. Finally, an overview of the clinical applications addressed with EM tracking is given. Throughout the paper, we report not only on scientific progress, but also provide a review on commercial systems. Given the continuous debate on the applicability of EM tracking in medicine, this paper provides a timely overview of the state-of-the-art in the field.

对象追踪是计算机辅助医学干预中的一项关键使能技术。通过连续定位医疗器械和患者解剖结构，它为深层解剖结构提供器械引导的前提条件。唯一广泛使用的、无需视线限制即可实时追踪小物体的技术是电磁（EM）追踪。尽管电磁追踪已成为许多研究的主题，但临床应用进展缓慢。因此，本综述旨在深入了解电磁追踪在医学中的未来潜力和局限性。我们描述了电磁追踪系统的基本工作原理，列出了主要误差来源，并总结了已发表的关于追踪精度、准确性和鲁棒性的研究以及相应的验证协议。此外，还深入综述了最新的误差补偿方法。最后，概述了电磁追踪所涉及的临床应用。全文不仅报告了科学进展，还对商业系统进行了综述。鉴于关于电磁追踪在医学中适用性的持续争论，本文及时概述了该领域的最新进展。

Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.

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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……

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 emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

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In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

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We investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

我们研究了一种基于支持向量机（SVM）的方法用于数字乳腺X线图像中微钙化（MC）簇的检测，并提出了一种连续增强学习方案以提高性能。SVM是一种基于结构风险最小化原理的机器学习方法，在处理训练集之外的数据时表现良好。我们将MC检测表述为一个监督学习问题，并应用SVM开发检测算法。我们使用SVM来检测图像中每个位置是否存在MC。我们使用包含1120个MC的76例临床乳腺X线图像数据库测试了所提出的方法。我们使用自由响应接收者操作特征曲线来评估检测性能，并将所提出的算法与几种现有方法进行比较。在我们的实验中，所提出的SVM框架优于所有其他测试方法。特别是，SVM方法在每个图像一个假阳性簇的错误率下达到了高达94%的灵敏度。SVM能够优于几种为广泛研究的MC检测问题而开发的知名方法，表明SVM是医学成像应用中物体检测的一种有前景的技术。

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...

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。

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

当前的计算机断层扫描（CT）技术允许在单次屏气内获取接近各向同性、亚毫米分辨率的完整胸部图像。这些薄层胸部扫描在胸部放射学中变得不可或缺，但也大大增加了放射科医生的数据负担。因此，自动化分析此类数据成为必要，这催生了一个快速发展的医学影像研究领域。本文综述了CT扫描中肺部计算机分析的文献，涵盖了各种肺部结构的分割、胸部扫描的配准，以及旨在检测、分类和量化胸部异常的应用。此外，还指出了研究趋势和挑战，并讨论了未来的研究方向。

An improved echo planar high-speed imaging technique using spiral scan is presented and experimental advantages are discussed. This proposed spiral-scan echo planar imaging (SEPI) technique employs two linearly increasing sinusoidal gradient fields, which results in a spiral trajectory in the spatial frequency domain (k-domain) that covers the entire frequency domain uniformly. The advantages of the method are: 1) circularly symmetric T2 weighting, resulting in a circularly symmetric point spread function in the image domain; 2) elimination of discontinuities in gradient waveforms which in turn will reduce initial transient as well as steady-state distortions; and 3) effective rapid spiral-scan from dc to high frequency in a continuous fashion, which ensures multiple pulsing with interlacing for further resolution improvement without T2 decay image degradation. Some preliminary experimental results will be presented and further possible improvements suggested.

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The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurys...

数字彩色眼底照片中微动脉瘤的检测是糖尿病视网膜病变（DR）自动化筛查的第一步，DR是糖尿病的常见并发症。过去已经发表了许多方法来实现这一检测，但没有一种方法在相同数据上进行比较。在这项工作中，我们展示了第一次国际微动脉瘤...

Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images.

图像分割是将图像划分为一组非重叠区域，这些区域的并集构成整个图像。图像被分解为具有某些特性（如灰度或纹理）一致的有意义部分。在本文中，我们提出了一种评估医学图像分割算法的方法，其中唯一可用的信息是由多位专家观察者勾画的边界。在这种情况下，分割算法的结果可以与多位观察者的勾画进行对比评估。我们推导了统计量，以判断计算机生成的边界与观察者手绘边界的一致性是否达到不同观察者之间的一致性水平。我们通过评估两种不同超声成像应用中的图像分割算法来说明该方法的使用。在第一个应用中，我们尝试从心脏超声图像中寻找心外膜和心内膜边界；在第二个应用中，我们的目标是从产前超声图像中寻找胎儿颅骨和腹部边界。

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function ...

心脏的三维（3-D）成像是医学成像中一个快速发展的研究领域。快速时空心脏成像的硬件和方法的进步正在拓展心血管疾病临床诊断和研究的边界。在过去几年中，许多方法被提出用于分析图像和提取心脏形状与功能的参数……

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, wh...

研究表明，使用多个图谱图像可以提高基于图谱的医学图像分割的准确性。每个图谱图像独立地配准到目标图像，计算出的变换应用于图谱图像的分割，以获得目标图像的分割版本。该过程产生多个独立的候选分割，...

We developed a general method for global conformal parameterizations based on the structure of the cohomology group of holomorphic one-forms for surfaces with or without boundaries (Gu and Yau, 2002), (Gu and Yau, 2003). For genus zero surfaces, our algorithm can find a unique mapping between any two genus zero manifolds by minimizing the harmonic energy of the map. In this paper, we apply the alg...

我们基于有界或无界曲面的全纯一次微分形式的同调群结构，发展了一种全局共形参数化的通用方法（Gu and Yau, 2002; Gu and Yau, 2003）。对于零亏格曲面，我们的算法可以通过最小化映射的调和能量，在任意两个零亏格流形之间找到唯一映射。在本文中，我们将该算法应用于...

Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” Thi...

在过去几年中，机器学习（或更广义的人工智能）引发了压倒性的研究兴趣，并吸引了前所未有的公众关注。作为断层成像研究人员，我们从成像角度分享了这一兴奋（见附录第1项），并组织了这期特刊，专注于“机器学习在图像重建中的应用”主题。本文...

The structure of microvasculature cannot be resolved using standard clinical ultrasound (US) imaging frequencies due to the fundamental diffraction limit of US waves. In this work, we use a standard clinical US system to perform in vivo sub-diffraction imaging on a CD1, female mouse aged eight weeks by localizing isolated US signals from microbubbles flowing within the ear microvasculature, and compare our results to optical microscopy. Furthermore, we develop a new technique to map blood velocity at super-resolution by tracking individual bubbles through the vasculature. Resolution is improved from a measured lateral and axial resolution of 112 μm and 94 μm respectively in original US data, to super-resolved images of microvasculature where vessel features as fine as 19 μm are clearly visualized. Velocity maps clearly distinguish opposing flow direction and separated speed distributions in adjacent vessels, thereby enabling further differentiation between vessels otherwise not spatially separated in the image. This technique overcomes the diffraction limit to provide a noninvasive means of imaging the microvasculature at super-resolution, to depths of many centimeters. In the future, this method could noninvasively image pathological or therapeutic changes in the microvasculature at centimeter depths in vivo.

由于超声波的固有衍射极限，标准临床超声成像频率无法解析微血管结构。在本工作中，我们使用标准临床超声系统，通过定位来自流经耳微血管的微泡的孤立超声信号，对一只八周龄的CD1雌性小鼠进行体内亚衍射成像，并将我们的结果与光学显微镜进行比较。此外，我们开发了一种新技术，通过追踪血管中的单个气泡来绘制超分辨血流速度。分辨率从原始超声数据中测量的横向112 μm和轴向94 μm提高到微血管超分辨图像，其中可清晰观察到细至19 μm的血管特征。速度图清晰区分相邻血管中的相反流动方向和分离的速度分布，从而进一步区分图像中原本未空间分离的血管。该技术突破了衍射极限，提供了一种非侵入性的超分辨微血管成像方法，深度可达数厘米。未来，该方法可无创地成像体内厘米深度处微血管的病理或治疗变化。

Presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. The authors assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further they assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. The authors present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

提出了一种新的校正磁共振成像（MRI）中强度不均匀性的方法，该方法显著改善了基于强度的组织分割。图像亮度值由于低频偏置场而失真，妨碍了视觉检查和分割。这种新的校正方法称为参数化偏置场校正（PABIC），基于一个简化模型……

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. Following recent developments in compressive sensing (CS) theory, several authors have demonstrated that certain classes of MR images which possess sparse representations in some transform domain can be accurately reconstructed from very highly undersampled K -space data by solving a convex lscr 1 -minimization problem. Although lscr 1 -based techniques are extremely powerful, they inherently require a degree of over-sampling above the theoretical minimum sampling rate to guarantee that exact reconstruction can be achieved. In this paper, we propose a generalization of the CS paradigm based on homotopic approximation of the lscr 0 quasi-norm and show how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound. Following a brief review of standard CS methods and the developed theoretical extensions, several example MRI reconstructions from highly undersampled K -space data are presented.

在临床磁共振成像（MRI）中，扫描时间的任何减少都能带来诸多潜在好处，从高时间速率观察生理过程到提高患者舒适度。随着压缩感知（CS）理论的最新发展，多位作者已经证明，通过求解一个凸的ℓ1最小化问题，可以从高度欠采样的K空间数据中准确重建出在某些变换域中具有稀疏表示的特定类别的MR图像。尽管基于ℓ1的技术非常强大，但它们本质上需要高于理论最小采样率的过采样程度，以确保能够实现精确重建。在本文中，我们提出了一种基于ℓ0拟范数同伦近似的CS范式的推广，并展示了如何将MR图像重建进一步推至奈奎斯特极限以下，并显著接近理论界限。在简要回顾标准CS方法和所发展的理论扩展之后，我们展示了几个从高度欠采样的K空间数据进行MRI重建的示例。

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained ...

稀疏视角计算机断层扫描（CT）在加速数据采集和减少CT扫描辐射剂量方面具有很大前景。最近稀疏视角CT重建算法的进展，如迭代重建算法，在获得高质量图像的同时需要先进的计算能力。近来，深度学习（DL）已广泛应用于各种应用，并取得了...

Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation ...

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We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

我们提出了一种用于医学图像噪声过滤的鲁棒小波域方法。该方法能够自适应地处理各种类型的图像噪声，并满足医学专家的偏好；通过单个参数，可以在保留（依赖于专家的）相关细节和降噪程度之间取得平衡。该算法利用关于显著图像特征在不同分辨率尺度上相关性的普遍有效知识，进行初步的系数分类。这种初步的系数分类用于经验性地估计代表有用图像特征的系数和主要代表噪声的系数的统计分布。通过使用局部空间活动的小波域指示器来实现对图像空间上下文的适应。该方法在实现和执行时间上都具有较低的复杂度。结果证明了它在医学超声和磁共振成像中噪声抑制的有效性。在这些应用中，无论在定量性能指标还是图像视觉质量方面，该方法都明显优于单分辨率空间自适应算法。

We employ the new geometric active contour models, previously formulated, for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a p...

我们采用先前提出的新型几何主动轮廓模型，用于磁共振成像（MRI）、计算机断层扫描（CT）和超声医学图像的边缘检测与分割。我们的方法基于在给定图像上定义基于特征的度量，这进而导致一种新的蛇模型范式，其中感兴趣的特征可以被视为位于一个...的底部。