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This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.

本文介绍了我们在设计用于结肠镜视频中息肉计算机辅助检测（CAD）系统的研究成果。我们的系统基于一种混合上下文-形状方法，利用上下文信息去除非息肉结构，并利用形状信息可靠地定位息肉。具体来说，给定一张结肠镜图像，我们首先获取粗略的边缘图。其次，通过我们独特的特征提取和边缘分类方案，从边缘图中去除非息肉边缘。第三，利用我们新颖的投票方案，在精细化的边缘图中以概率置信度分数定位息肉候选区域。所提出的CAD系统使用两个公开的息肉数据库进行了测试：CVC-ColonDB包含300张结肠镜图像，来自15个不同息肉的共300个息肉实例；以及ASU-Mayo数据库，这是我们收集的结肠镜视频，包含19,400帧，来自10个不同息肉的共5,200个息肉实例。我们使用自由响应受试者工作特征（FROC）分析评估了系统。在每帧0.1个假阳性时，系统在CVC-ColonDB上的灵敏度为88.0%，在ASU-Mayo数据库上的灵敏度为48%。此外，我们使用一种新的检测延迟分析来评估系统，延迟定义为从息肉首次出现在结肠镜视频中到系统首次检测到它的时间。在每帧0.05个假阳性时，系统的息肉检测延迟为0.3秒。

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

显微细胞图像分析是生物学研究的基本工具。特别是，多变量荧光显微镜用于观察培养细胞的不同方面。目前，通过视觉检查单个细胞来执行分析任务仍然是常见做法，这既耗时又容易产生主观偏差。这使得自动细胞图像分析对于大规模、客观的细胞培养研究至关重要。传统上，自动细胞分析的任务通过使用图像分割方法提取细胞的位置和形状来实现。图像分割虽然是基础，但在计算机视觉中并非易事，也不鲁棒于图像质量变化。这使得用于细胞检测的图像分割半自动化，需要频繁调整参数。本文提出了一种基于滑动带滤波器（SBF）的多变量图像中细胞检测和形状估计的新方法。该滤波器的设计使其足以检测整体凸形状，因此在细胞检测中表现良好。此外，涉及的参数直观，因为它们直接与预期的细胞大小相关。使用SBF滤波器，我们检测细胞核和细胞质的位置和形状。基于每个细胞在细胞核和细胞质荧光通道中具有近似相同形状中心的假设，我们通过核检测指导细胞质形状估计，提高性能并减少误差。然后通过从核和细胞质通道收集证据来验证细胞检测。此外，我们还包括重叠校正和形状正则化步骤，进一步改进估计的细胞形状。该方法使用两个不同类型的数据集进行评估：一个包含1000个模拟细胞的20张模拟细胞培养图像基准集；一个包含1255个细胞、标记DNA和肌动蛋白的16张黑腹果蝇Kc167数据集。由于细胞形状的高度变异和细胞之间频繁的簇重叠，这两个图像数据集都提出了难题。在果蝇数据集上，我们的方法在核检测和细胞质检测上分别达到了95%/69%和82%/90%的精确率/召回率，总体准确率为76%。

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN)...

使用标准苏木精和伊红染色的癌组织病理学图像中细胞核的检测和分类由于细胞异质性而具有挑战性。深度学习已在多项研究中显示出对组织病理学图像产生令人鼓舞的结果。在本文中，我们提出了一种空间约束卷积神经网络（SC-CNN）...

Previously unknown sufficient conditions, a necessary condition, and reconstruction methods for image reconstruction from cone-beam projections are developed. A sufficient condition developed is contained in the following statement. Statement 5: If one every plane that intersects the object, there exists at least one cone-beam source point, then the object can be reconstructed. Reconstruction meth...

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Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, the authors first discuss the widely used methods based on...

功能磁共振成像（fMRI）提供高分辨率数据集，使研究人员能够获得认知过程中激活区域的准确描绘和敏感检测。为了保持这种无创技术的分辨率，在数据分析中需要精细的方法。本文首先讨论了基于...的广泛使用的方法。

From a set of longitudinal three-dimensional scans of the same anatomical structure, the authors have accurately modeled the temporal shape and size changes using a linear shape model. On a total of 31 computed tomography scans of the mandible from six patients, 14,851 semilandmarks are found automatically using shape features and a new algorithm called geometry-constrained diffusion. The semiland...

作者利用一组同一解剖结构的纵向三维扫描，通过线性形状模型精确模拟了时间上的形状和大小变化。基于六名患者的31次下颌骨CT扫描，使用形状特征和一种名为几何约束扩散的新算法自动找到了14851个半地标。半地标...

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D p...

我们提出了一种新颖的计算机辅助检测（CAD）系统，用于肺结节的检测，该系统使用多视角卷积网络（ConvNets），自动从训练数据中学习判别特征。网络输入是通过结合三个专门为实性、亚实性和大结节设计的候选检测器获得的结节候选。对于每个候选，一组二维...

A shape-based interpolation scheme for multidimensional images is presented. This scheme consists of first segmenting the given image data into a binary image, converting the binary image back into a gray image wherein the gray value of a point represents its shortest distance (positive value for points of the object and negative for those outside) from the cross-sectional boundary, and then inter...

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In this paper, we propose DeepCut , a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.

在本文中，我们提出了DeepCut，一种从带有弱标注（即边界框）的图像数据集中获得逐像素对象分割的方法。它扩展了著名的GrabCut[1]方法，通过从边界框标注中训练神经网络分类器，将机器学习纳入其中。我们将问题表述为在密集连接的条件随机场上的能量最小化问题，并迭代更新训练目标以获得逐像素对象分割。此外，我们提出了DeepCut方法的变体，并将其与弱监督下CNN训练的朴素方法进行比较。我们在一个具有挑战性的胎儿磁共振数据集上测试了其解决脑和肺分割问题的适用性，并在准确性方面获得了令人鼓舞的结果。

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions. The ...

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This paper presents microwave tomographic reconstructions of the complex permittivity of lossy dielectric objects immersed in water from experimental multiview near-field data obtained with a 2.35-GHz planar active microwave camera. An iterative reconstruction algorithm based on the Levenberg-Marquardt method was used to solve the nonlinear matrix equation which results when applying a moment meth...

本文介绍了利用2.35 GHz平面有源微波相机获得的实验多视近场数据，对浸没在水中的有耗介电物体的复介电常数进行微波层析重建。采用基于Levenberg-Marquardt方法的迭代重建算法来求解应用矩量法时产生的非线性矩阵方程...

We give criteria for the distinguishability of two different conductivity distributions inside a body by electric current computed tomography (ECCT) systems with a specified precision. It is shown in a special case how these criteria can be used to determine the measurement precision needed to distinguish between two different conductivity distributions. It is also shown how to select the patterns...

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We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.

我们提出了一种基于模型的图像重建框架，采用卷积神经网络（CNN）正则化先验。该公式提供了一种系统的方法，用于推导任意结构逆问题的深度学习架构。由于正向模型被明确考虑，与直接反演方法相比，一个参数更少的小型网络就足以捕获图像信息，从而减少对训练数据和训练时间的需求。由于我们依赖跨迭代共享权重的端到端训练，CNN权重是根据正向模型定制的，因此相比依赖预训练去噪器的方法提供了更好的性能。我们的实验表明，这种方法将迭代次数与网络复杂性分离，带来了好处，包括降低训练数据需求、减少过拟合风险以及显著减少内存占用的实现。我们建议通过在网络内使用数值优化块（例如共轭梯度算法）来强制执行数据一致性。与依赖近端梯度步骤来强制执行数据一致性的方法相比，这种方法每次迭代收敛更快。我们的实验表明，更快的收敛转化为更好的性能，尤其是在可用GPU内存限制迭代次数时。

By exploiting the correlation of ultrasound speckle patterns that result from scattering by underlying tissue elements, two-dimensional tissue motion theoretically can be recovered by tracking the apparent movement of the associated speckle patterns. Speckle tracking, however, is an ill-posed inverse problem because of temporal decorrelation of the speckle patterns and the inherent low signal-to-n...

通过利用由底层组织元素散射产生的超声斑点模式的相关性，理论上可以通过跟踪相关斑点模式的表观运动来恢复二维组织运动。然而，由于斑点模式的时间去相关性和固有的低信噪比，斑点跟踪是一个不适定的逆问题。

Images produced in emission tomography with the expectation-maximization algorithm have been observed to become more noisy and to have large distortions near edges as iterations proceed and the images converge towards the maximum-likelihood estimate. It is our conclusion that these artifacts are fundamental to reconstructions based on maximum-likelihood estimation as it has been applied usually; t...

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This paper describes research work motivated by an innovative medical application: computer-assisted transbronchial biopsy. This project involves the registration, with no external localization device, of a preoperative three-dimensional (3-D) computed tomography (CT) scan of the thoracic cavity (showing a tumor that requires a needle biopsy), and an intraoperative endoscopic two-dimensional (2-D)...

本文描述了由一项创新医学应用（计算机辅助经支气管活检）所驱动的研究工作。该项目涉及在无外部定位设备的情况下，将术前三维胸部CT扫描（显示需要针吸活检的肿瘤）与术中二维内镜图像进行配准...

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net ) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net , a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

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We propose a new white matter atlas creation method that learns a model of the common white matter structures present in a group of subjects. We demonstrate that our atlas creation method, which is based on group spectral clustering of tractography, discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas that we call a high-dimensional white matter atlas. We then show how to perform automatic segmentation of tractography from novel subjects by extending the spectral clustering solution, stored in the atlas, using the Nystrom method. We present results regarding the stability of our method and parameter choices. Finally we give results from an atlas creation and automatic segmentation experiment. We demonstrate that our automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, enabling group comparison of white matter anatomy.

我们提出一种新的白质图谱创建方法，该方法学习一组受试者中常见白质结构的模型。我们证明了基于纤维束成像群体谱聚类的图谱创建方法能够发现与预期白质解剖结构相对应的结构，例如胼胝体、钩束、扣带束、弓状束和放射冠。白质簇附有专家解剖标签，并存储在我们称为高维白质图谱的新型图谱中。然后，我们展示了如何通过使用Nyström方法扩展图谱中存储的谱聚类解决方案，对新受试者的纤维束成像进行自动分割。我们给出了关于方法稳定性和参数选择的结果。最后，我们展示了图谱创建和自动分割实验的结果。我们证明，我们的自动纤维束成像分割能够识别跨半球和跨受试者的对应白质区域，从而实现白质解剖的组间比较。

A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to reconstruct artifact-free images. The image reconstructed from estimates using this tec...

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Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

数字显微组织图像中的细胞核分割能够提取高质量的特征，用于核形态学测量和计算病理学中的其他分析。传统的图像处理技术，如Otsu阈值分割和分水岭分割，在染色质稀疏和密集细胞核等具有挑战性的情况下效果不佳。相比之下，基于机器学习的分割可以泛化到各种细胞核外观。然而，训练机器学习算法需要大量已标注细胞核的图像数据集。公开可访问的标注数据集，以及广泛认可的用于比较技术的指标，已经极大地推动了其他图像分类问题（尤其是对象识别）的创新和进展。受其成功启发，我们引入了一个大型公开可访问的苏木精和伊红（H&E）染色组织图像数据集，其中包含超过21000个精心标注的细胞核边界，其质量由医学医生验证。由于我们的数据集来自多家医院，并包含来自多个患者、疾病状态和器官的多种细胞核外观，基于该数据集训练的技术很可能具有良好的泛化能力，并可直接应用于其他H&E染色图像。我们还提出了一种新的评估细胞核分割结果的指标，该指标统一惩罚对象级和像素级错误，而以往的指标只惩罚一种错误。我们还提出了一种基于深度学习的分割技术，特别强调识别细胞核边界，包括接触或重叠的细胞核之间的边界，并且在各种测试图像上表现良好。

We present here a new method to identify the position of the optic disc (OD) in retinal fundus images. The method is based on the preliminary detection of the main retinal vessels. All retinal vessels originate from the OD and their path follows a similar directional pattern (parabolic course) in all images. To describe the general direction of retinal vessels at any given position in the image, a geometrical parametric model was proposed, where two of the model parameters are the coordinates of the OD center. Using as experimental data samples of vessel centerline points and corresponding vessel directions, provided by any vessel identification procedure, model parameters were identified by means of a simulated annealing optimization technique. These estimated values provide the coordinates of the center of OD. A Matlab/spl reg/ prototype implementing this method was developed. An evaluation of the proposed procedure was performed using the set of 81 images from the STARE project, containing images from both normal and pathological subjects. The OD position was correctly identified in 79 out of 81 images (98%), even in rather difficult pathological situations.

我们在此提出一种新方法，用于识别视网膜眼底图像中视盘（OD）的位置。该方法基于对视网膜主要血管的初步检测。所有视网膜血管都起源于视盘，并且它们的路径在所有图像中都遵循类似的方向模式（抛物线走向）。为了描述图像中任何给定位置视网膜血管的一般方向，提出了一个几何参数模型，其中两个模型参数是视盘中心的坐标。利用任何血管识别程序提供的血管中心线点及其对应方向的实验数据样本，通过模拟退火优化技术识别模型参数。这些估计值提供了视盘中心的坐标。开发了实现该方法的Matlab原型。使用STARE项目中的81张图像（包含正常和病变受试者的图像）对所提方法进行了评估。在81张图像中，有79张（98%）正确识别了视盘位置，即使在相当困难的病变情况下也如此。

The distorted Born iterative method (DBIM) is used to solve two-dimensional inverse scattering problems, thereby providing another general method to solve the two-dimensional imaging problem when the Born and the Rytov approximations break down. Numerical simulations are performed using the DBIM and the method proposed previously by the authors (Int. J. Imaging Syst. Technol., vol.1, no.1, p.100-8...

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In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their ...

本文采用多分辨率分析，特别是离散小波变换模极大值（mod-max）方法，用于提取乳腺肿块形状特征。这些形状特征用于分类系统，将肿块分为圆形、结节状或星状。将多分辨率形状特征与传统的单分辨率形状特征进行比较...

Image interpolation techniques often are required in medical imaging for image generation (e.g., discrete back projection for inverse Radon transform) and processing such as compression or resampling. Since the ideal interpolation function spatially is unlimited, several interpolation kernels of finite size have been introduced. This paper compares 1) truncated and windowed sine; 2) nearest neighbor; 3) linear; 4) quadratic; 5) cubic B-spline; 6) cubic; g) Lagrange; and 7) Gaussian interpolation and approximation techniques with kernel sizes from 1/spl times/1 up to 8/spl times/8. The comparison is done by: 1) spatial and Fourier analyses; 2) computational complexity as well as runtime evaluations; and 3) qualitative and quantitative interpolation error determinations for particular interpolation tasks which were taken from common situations in medical image processing. For local and Fourier analyses, a standardized notation is introduced and fundamental properties of interpolators are derived. Successful methods should be direct current (DC)-constant and interpolators rather than DC-inconstant or approximators. Each method's parameters are tuned with respect to those properties. This results in three novel kernels, which are introduced in this paper and proven to be within the best choices for medical image interpolation: the 6/spl times/6 Blackman-Harris windowed sinc interpolator, and the C2-continuous cubic kernels with N=6 and N=8 supporting points. For quantitative error evaluations, a set of 50 direct digital X-rays was used. They have been selected arbitrarily from clinical routine. In general, large kernel sizes were found to be superior to small interpolation masks. Except for truncated sine interpolators, all kernels with N=6 or larger sizes perform significantly better than N=2 or N=3 point methods (p/spl Lt/0.005). However, the differences within the group of large-sized kernels were not significant. Summarizing the results, the cubic 6/spl times/6 interpolator with continuous second derivatives, as defined in (24), can be recommended for most common interpolation tasks. It appears to be the fastest six-point kernel to implement computationally. It provides eminent local and Fourier properties, is easy to implement, and has only small errors. The same characteristics apply to B-spline interpolation, but the 6/spl times/6 cubic avoids the intrinsic border effects produced by the B-spline technique. However, the goal of this study was not to determine an overall best method, but to present a comprehensive catalogue of methods in a uniform terminology, to define general properties and requirements of local techniques, and to enable the reader to select that method which is optimal for his specific application in medical imaging.

图像插值技术在医学成像中常用于图像生成（例如，用于逆拉东变换的离散反投影）以及处理如压缩或重采样。由于理想插值函数在空间上是无限的，因此引入了多种有限大小的插值核。本文比较了：1）截断和加窗sinc；2）最近邻；3）线性；4）二次；5）三次B样条；6）三次；7）拉格朗日；以及8）高斯插值和近似技术，核大小从1×1到8×8。比较通过以下方式进行：1）空间和傅里叶分析；2）计算复杂度及运行时间评估；以及3）针对医学图像处理中常见情况的特定插值任务的定性和定量插值误差确定。对于局部和傅里叶分析，引入了标准化符号并推导了插值器的基本属性。成功的方法应为直流（DC）恒定且为插值器，而非DC不恒定或近似器。每种方法的参数根据这些属性进行调整。这产生了三个新颖的核，在本文中介绍并证明是医学图像插值的最佳选择之一：6×6 Blackman-Harris窗sinc插值器，以及具有N=6和N=8支撑点的C2连续三次核。对于定量误差评估，使用了50张直接数字X光片。这些X光片是从临床常规中任意选取的。总的来说，大核尺寸优于小插值掩模。除截断sinc插值器外，所有N=6或更大尺寸的核性能显著优于N=2或N=3点方法（p<<0.005）。然而，大尺寸核组内的差异并不显著。总结结果，如（24）中定义的具有连续二阶导数的三次6×6插值器可推荐用于大多数常见插值任务。它似乎是计算上最快的六点核。它提供了卓越的局部和傅里叶特性，易于实现，且误差很小。相同的特性也适用于B样条插值，但6×6三次插值避免了B样条技术产生的固有边界效应。然而，本研究的目标并非确定总体最佳方法，而是以统一的术语提供方法的全面目录，定义局部技术的一般属性和要求，并使读者能够选择最适合其特定医学成像应用的方法。

Optic disc (OD) detection is a main step while developing automated screening systems for diabetic retinopathy. We present in this paper a method to automatically detect the position of the OD in digital retinal fundus images. The method starts by normalizing luminosity and contrast through out the image using illumination equalization and adaptive histogram equalization methods respectively. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Hence, a simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity. The retinal vessels are segmented using a simple and standard 2-D Gaussian matched filter. Consequently, a vessels direction map of the segmented retinal vessels is obtained using the same segmentation algorithm. The segmented vessels are then thinned, and filtered using local intensity, to represent finally the OD-center candidates. The difference between the proposed matched filter resized into four different sizes, and the vessels' directions at the surrounding area of each of the OD-center candidates is measured. The minimum difference provides an estimate of the OD-center coordinates. The proposed method was evaluated using a subset of the STARE project's dataset, containing 81 fundus images of both normal and diseased retinas, and initially used by literature OD detection methods. The OD-center was detected correctly in 80 out of the 81 images (98.77%). In addition, the OD-center was detected correctly in all of the 40 images (100%) using the publicly available DRIVE dataset.

视盘检测是开发糖尿病视网膜病变自动筛查系统的主要步骤。本文提出了一种自动检测数字视网膜眼底图像中视盘位置的方法。该方法首先通过光照均衡化和自适应直方图均衡化分别对图像进行亮度和对比度归一化。视盘检测算法基于匹配视网膜血管的预期方向模式。因此，提出了一种简单的匹配滤波器来粗略匹配视盘附近血管的方向。使用简单且标准的二维高斯匹配滤波器对视网膜血管进行分割。随后，使用相同的分割算法获得分割后的视网膜血管的方向图。然后对分割后的血管进行细化，并使用局部强度进行滤波，最终得到视盘中心候选点。测量所提出的匹配滤波器（调整成四种不同尺寸）与每个视盘中心候选点周围区域的血管方向之间的差异。最小差异提供了视盘中心坐标的估计。使用STARE项目数据集的一个子集对所提出的方法进行了评估，该子集包含81张正常和病变视网膜的眼底图像，并且最初被文献中的视盘检测方法使用。在81张图像中，有80张（98.77%）正确检测到了视盘中心。此外，使用公开可用的DRIVE数据集，在40张图像中全部（100%）正确检测到了视盘中心。