Motion estimation in image sequences using the deformation ofapparent contours

The problem of determining the camera motion from apparent contours or silhouettes of a priori unknown curved 3D surfaces is considered. In a sequence of images, it is shown how to use the generalized epipolar constraint on apparent contours. One such constraint is obtained for each epipolar tangency point in each image pair. An accurate algorithm for computing the motion is presented based on a maximum likelihood estimate. It is shown how to generate initial estimates on the camera motion using only the tracked contours. It is also shown that in theory the motion can be calculated from the deformation of a single contour. The algorithm has been tested on several real image sequences, for both Euclidean and projective reconstruction. The resulting motion estimate is compared to motion estimates calculated independently using standard feature-based methods. The motion estimate is also used to classify the silhouettes as curves or apparent contours. The statistical evaluation shows that the technique gives accurate and stable results     

Structure-preserving smoothing of biomedical images

Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood.In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.     

Interactive 3D medical image segmentation with energy-minimizing implicit functions

We present an interactive segmentation method for 3D medical images that reconstructs the surface of an object using energy-minimizing, smooth, implicit functions. This reconstruction problem is called variational interpolation. For an intuitive segmentation of medical images, variational interpolation can be based on a set of user-drawn, planar contours that can be arbitrarily oriented in 3D space. This also allows an easy integration of the algorithm into the common manual segmentation workflow, where objects are segmented by drawing contours around them on each slice of a 3D image.Because variational interpolation is computationally expensive, we show how to speed up the algorithm to achieve almost real-time calculation times while preserving the overall segmentation quality. Moreover, we show how to improve the robustness of the algorithm by transforming it from an interpolation to an approximation problem and we discuss a local interpolation scheme.A first evaluation of our algorithm by two experienced radiology technicians on 15 liver metastases and 1 liver has shown that the segmentation times can be reduced by a factor of about 2 compared to a slice-wise manual segmentation and only about one fourth of the contours are necessary compared to the number of contours necessary for a manual segmentation.     

Active contours methods with respect to Vickers indentations

We investigate different Vickers indentation segmentation methods and especially concentrate on active contours approaches as these techniques are known to be precise state of the art segmentation methods. Particularly, different kinds of level set-based methods which are improvements of the traditional active contours are analyzed. In order to circumvent the initialization problem of active contours, we separate the segmentation process into two stages. For the first stage, we introduce an approach which approximately locates the indentations with a high certainty. The results achieved with this method serve as initializations for the precise active contours (second stage). This two-stage approach delivers highly precise results for most real world indentation images. However, there are images, which are very difficult to segment. To handle even such images, our segmentation method is incorporated with the Shape from Focus approach, by including 3D information. In order to decrease the overall runtime, moreover, a gradual enhancement approach based on unfocused images is introduced. With three different databases, we compare the proposed methods and we show that the segmentation accuracy of these methods is highly competitive compared with other approaches in the literature.     

Translation, Scale, and Deformation Weighted Polar Active Contours

Polar active contours have proven to be a powerful segmentation method for many medical as well as other computer vision applications, such as interactive image segmentation or tracking. Inspired by recent work on Sobolev active contours we derive a Sobolev-type function space for polar curves, which is endowed with a metric that allows us to favor origin translations and scale changes over smooth deformations of the curve. The resulting translation, scale, and deformation weighted polar active contours inherit the coarse-to-fine behavior of Sobolev active contours as well as their robustness to local minima and are thus very useful for many medical applications, such as cross-sectional vessel segmentation, aneurysm analysis, or cell tracking.     

Multiregion level-set partitioning of synthetic aperture radar images

The purpose of this study is to investigate synthetic aperture radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.     

Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images

We present a variational framework for naturally incorporating prior shape knowledge in guidance of active contours for boundary extraction in images. This framework is especially suitable for images collected outside the visible spectrum, where boundary estimation is difficult due to low contrast, low resolution, and presence of noise and clutter. Accordingly, we illustrate this approach using the segmentation of various objects in synthetic aperture sonar (SAS) images of underwater terrains. We use elastic shape analysis of planar curves in which the shapes are considered as elements of a quotient space of an infinite dimensional, non-linear Riemannian manifold. Using geodesic paths under the elastic Riemannian metric, one computes sample mean and covariances of training shapes in each classes and derives statistical models for capturing class-specific shape variability. These models are then used as shape priors in a variational setting to solve for Bayesian estimation of desired contours as follows. In traditional active contour models curves are driven towards minimum of an energy composed of image and smoothing terms. We introduce an additional shape term based on shape models of relevant shape classes. The minimization of this total energy, using iterated gradient-based updates of curves, leads to an improved segmentation of object boundaries. This is demonstrated using a number of shape classes in two large SAS image datasets.     

A narrow band graph partitioning method for skin lesion segmentation

Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. In this paper, we present a novel multi-modal skin lesion segmentation method based on region fusion and narrow band energy graph partitioning. The proposed method can handle challenging characteristics of skin lesions, such as topological changes, weak or false edges, and asymmetry. Extensive testing demonstrated that in this method complex contours are detected correctly while topological changes of evolving curves are managed naturally. The accuracy of the method was quantified using a lesion similarity measure and lesion segmentation error ratio, Our results were validated using a large set of epiluminescence microscopy (ELM) images acquired using cross-polarization ELM and side-transillumination ELM. Our findings demonstrate that the new method can achieve improved robustness and better overall performance compared to other state-of-the-art segmentation methods.     

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.     

Combined evolution of level sets and B-spline curves for imaging

We propose the evolution of curves in direction of their unit normal using a combined implicit and explicit spline representation according to a given velocity field. In the implicit case we evolve a level set function for segmentation and geometry reconstruction in 2D images. The level set approach allows for topological changes of the evolving curves. The evolution of the explicit B-spline curve is driven by the Mumford–Shah functional. We are mainly concerned with the segmentation of images using active contours. To get satisfactory results from the implicit evolution the optimal stopping time and the correct level of the evolving function has to be estimated. We overcome this problem by using the combined evolution.     

Anatomical structure modeling from medical images

Some clinical applications, such as surgical planning, require volumetric models of anatomical structures represented as a set of tetrahedra. A practical method of constructing anatomical models from medical images is presented. The method starts with a set of contours segmented from the medical images by a clinician and produces a model that has high fidelity with the contours. Unlike most modeling methods, the contours are not restricted to lie on parallel planes. The main steps are a 3D Delaunay tetrahedralization, culling of non-object tetrahedra, and refinement of the tetrahedral mesh. The result is a high-quality set of tetrahedra whose surface points are guaranteed to match the original contours. The key is to use the distance map and bit volume structures that were created along with the contours. The method is demonstrated on computed tomography, MRI and 3D ultrasound data. Models of 170,000 tetrahedra are constructed on a standard workstation in approximately 10s. A comparison with related methods is also provided.     

Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.     

保持轮廓清晰光滑的灰度图像放大算法

灰度图像放大时，插值所具有的平滑作用会退化图像的高频部分，使放大图像轮廓变得模糊，文中提出一种基于拟合分界线的插值放大算法，该处包括分割和插值放大两个步骤；分割是搜索出灰度图像的突变象素点，并用三次均匀B样条把它们拟合为光滑分界线，以把整幅图像分割为若干子区域；插值放大是基于拟合分界线对图像插值，即插值操作限定于原图像的某一子区域内进行。采用文中算法得到的放大图像不仅可保持轮廓清晰，而且可持续轮廓光滑，最后给出三个放大实例，证明了 文中放大算法比常规算法产生的图像质量高。     

Contour-driven Sumi-e rendering of real photos

We propose a sketch-based system for rendering oriental brush strokes on complex shapes. While previous research has focused on methods for converting user-specified trajectories into oriental ink painting (Sumi-e) strokes, we propose an approach that takes as input the contours of complex shapes, estimates automatically the sizes of the brush footprints, their orientations, and the brush trajectory. Then, it renders them into oriental ink paintings. The approach performs in three steps; first complex shapes are decomposed into elementary shapes that can be rendered with a single brush stroke. Second, the brush trajectory estimation is formulated as the minimization of an energy function that measures the quality of the trajectory. Finally, the estimated trajectories are rendered into brush strokes by mapping on them footprint textures scanned from real images. We demonstrate that the proposed contour-driven approach is particularly suitable for converting real images into Sumi-e paintings with minimum interaction. To this end we develop a system where images are either interactively or automatically segmented into elementary shapes and converted into Sumi-e paintings. For the automatic segmentation, we observe that the strokes in Sumi-e paintings are nearly parallel to the edge tangent flow (ETF). We propose a segmentation procedure that is based on clustering the curves obtained by integrating the ETF of real images. Experiments on complex shapes show that the proposed contour-based approach produces a large variety of complex strokes compared to trajectory-based approaches.     

Semi-automated CT segmentation using optic flow and Fourier interpolation techniques

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.     

Semi-automated CT segmentation using optic flow and Fourier interpolation techniques

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.     

Meshing interfaces of multi-label data with Delaunay refinement

In medical imaging, the generation of surface representations of anatomical objects obtained by labeling images from various modalities, is a critical component for visualization, simulation, and analysis. The interfaces between labeled regions can meet at arbitrary angles and with complex topologies, causing most automatic meshing algorithms to fail. We apply a recent Delaunay refinement algorithm to generate high quality triangular meshes that approximate the interface surfaces. This algorithm has proven guarantees for meshing piecewise-smooth shapes and its implementation overhead is low. Consequently, the approach is applicable to labeled datasets generated from binary segmentations as well as from probabilistic segmentation algorithms. We show the effectiveness of this technique on data from a variety of medical fields and discuss its ability to control the quality and size of the output meshes. The same algorithm can be used to generate tetrahedral meshes of the segmentation space.     

多级水脊线算法在二维医学超声图像中的应用

以肝囊肿超声图像为例,介绍一种新的医学超声图像的分割方法：多级水脊线分割算法在医学二维超声图像分割中的应用。由于斑点噪声和超声衰减产生的假象,二维和三维超声成像中的图像分割问题一直是公认的一大难点。本文采用由若干数学形态学辅助的水脊线方法提取超声图像中的肝囊肿边缘,取得了相当令人满意的结果,这为进一步的图像处理和化疗等治疗手段奠定了必要的基础。该方法自动选择并分割图像中的主要目标对象,从而克服了传统水脊线算法中常见的过分割和改进水脊线算法中半自动的缺陷。     

Shape "break-and-repair" strategy and its application to automated medical image segmentation

In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed "break-and-repair" is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape "break-and-repair" strategy in medical image segmentation.     

肺部影像解剖结构分割数据集及应用

目的 从影像中快速精准地分割出肺部解剖结构可以清晰直观地分辨各解剖结构间的关系,提供有效、客观的辅助诊断信息,大大提高医生的阅片效率并降低医生的工作量。随着影像分割算法的发展,越来越多的方法应用于分割肺部影像中感兴趣的解剖结构区域,但目前尚缺乏包含多种肺部精细解剖结构的影像数据集。本文创建了一个带标签的肺部CT/CTA （computer tomography/computer tomography angiography）影像数据集,以促进肺部解剖结构分割算法的发展。方法 该数据集共标记了67组肺部CT/CTA影像,包括CT影像24组、CTA影像43组,共计切片图像26 157幅。每组CT/CTA有4个不同的目标区域类别,标记对应支气管、肺实质、肺叶、肺动脉和肺静脉。结果 本文利用该数据集,用于肺部CT解剖结构分割医学影像挑战赛——2020年第四届国际图像计算与数字医学研讨会,该挑战赛提供了一个肺血管、支气管和肺实质的评估平台,通过Dice系数、过分割率、欠分割率、医学和算法行业专家对分割和3维重建效果进行了评估,目的是比较各种算法分割肺部解剖结构的性能。结论 本文详细描述了包括支气管、肺实质、肺叶、肺动脉和肺静脉等解剖结构标签的肺部影像数据集和应用结果,为相关研究人员利用本数据集进行更深入的研究提供参考。