Evolution of Co-dimension k>1 Manifolds Embedded in R^n and Applications to Medical Image Analysis

共维 k 的演化

• 批准号: 9972228
• 金额: $ 20.25万
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9972228&HistoricalAwards=false
• 关键词: Evolution; Co; dimension; k& gt

Faugeras9972228 The investigators and their colleagues study the problem ofautomatically clustering sets of points that lie on smoothmanifolds of arbitrary co-dimension. They use the geodesic snakeapproach in which they set up a Partial Differential Equation(PDE) that drives the evolution of a manifold in some potentialfield created by the data points, such that the solution of thePDE converges to a manifold that is very close to the data points(ideally contains them). The "shape" of this manifold (includingits co-dimension) characterizes the set of points. Theyinvestigate computationally efficient implementations forevolving manifolds of arbitrary co-dimension by level setsmethods, because of their robustness and flexibility. Theseimplementations are being used to study the Euclidean and affinemean-curvature motion of curves in three-dimensional spaces. Twoapplications are being pursued by the investigators. The firstapplication is the detection of blood vessels in MagneticResonance Angiography (MRA) images. In MRA images, blood vesselsappear as bright and noisy curve-like patterns, possibly withgaps. It is important to detect those patterns independently ofthe noise and to bridge the gaps. The detection of blood vesselscan be considered, as a first approximation, as an instance ofthe three-dimensional case, co-dimension two. The secondapplication is the clustering of feature points in higherdimensional spaces with an eye on two specific problems, thedescription of the "shape" of a cluster and the design ofautomated content-based image retrieval systems. A given pattern,e.g. an image, can be represented by a vector of features in somen-dimensional space. Patterns in the same class usually fall intoclusters that may have some very tricky "shapes". Theinvestigators and their collegues believe that their methods canprovide ways of automatically obtaining shape descriptions fromsets of sample feature vectors thereby facilitating the design ofrecognition algorithms and improving their performances. A targetapplication is the design of automated content-based imageretrieval systems. The investigators and their colleagues study the problem ofautomatically fitting a set of points in a possiblyhigh-dimensional space to a set of smooth manifolds. This isimportant because in many practical applications data can berepresented as points in high-dimensional spaces. They arecurrently pursuing two applications of these techniques. Thefirst one is the detection of blood vessels in Magnetic ResonanceAngiography (MRA) images. In MRA images, blood vessels appear asbright and noisy curve-like patterns, possibly with gaps. It isimportant to detect those patterns independently of the noise andto bridge the gaps. The results of this detection can be used inthe diagnosis of diseases and in planning surgery. The secondapplication is the description of the "shape" of a cluster ofpoints in a high-dimensional space. The investigators and theircolleagues are investigating means of automatically obtainingsuch descriptions from a set of samples. They believe that thiswill facilitate the design of recognition algorithms and increasetheir performances. A target application that is particularlyrelevant to the average citizen is the design of automatedcontent-based image retrieval systems. In such systems each imagecan be represented by a point in some high-dimensional (10 to 20)feature space, and images in the same class, e.g. images of cars,cluster on some manifold, e.g a "car-manifold". The descriptionof the shape of this manifold (which is simpler than the realshapes of the objects in the class) can then be used to retrieveall images of, e.g. cars, in the data base because theirrepresentations in the high-dimensional space will be closer tothe corresponding manifold than to any other.

Faugeras9972228 研究人员和他们的同事们研究了自动聚类问题，这些点位于任意余维的光滑流形上。 他们使用测地线蛇方法，在该方法中，他们建立了一个偏微分方程（PDE），该方程驱动由数据点创建的某些势场中的流形的演化，使得PDE的解收敛到非常接近数据点（理想情况下包含它们）的流形。 这个流形的“形状”（包括它的余维）表征了点的集合。 他们调查计算效率的实现forvolving流形的任意余维水平集方法，因为他们的鲁棒性和灵活性。 这些实现被用于研究三维空间中曲线的欧几里德和仿射曲率运动。 调查人员正在追查两项申请。 第一个应用是磁共振血管造影（MRA）图像中的血管检测。 在MRA图像中，血管表现为明亮且嘈杂的曲线状图案，可能存在间隙。 重要的是独立于噪声检测这些模式并弥合差距。 血管的检测可以被认为是，作为第一近似，作为三维情况的一个实例，余维2。 第二个应用是在高维空间中对特征点进行聚类，着眼于两个具体问题，即聚类的“形状”描述和基于内容的自动图像检索系统的设计。 一个给定的模式，例如一个图像，可以用一维空间中的特征向量来表示。 同一类中的模式通常属于可能有一些非常棘手的“形状”的集群。 研究者和他们的同事们相信，他们的方法可以提供从样本特征向量集合中自动获得形状描述的方法，从而促进识别算法的设计并提高其性能. 目标应用程序是基于内容的自动图像检索系统的设计。 研究人员和他们的同事研究了将可能高维空间中的一组点自动拟合到一组光滑流形的问题。 这一点很重要，因为在许多实际应用中，数据可以表示为高维空间中的点。 他们目前正在研究这些技术的两种应用。 第一部分是磁共振血管成像（MRA）图像中血管的检测。 在MRA图像中，血管表现为明亮而嘈杂的曲线状图案，可能存在间隙。 重要的是独立于噪声检测这些模式并弥合差距。 该检测结果可用于疾病诊断和手术计划的制定。 第二个应用是描述高维空间中的点簇的“形状”。 研究人员和他们的同事正在研究从一组样本中自动获取此类描述的方法。 他们相信这将促进识别算法的设计并提高其性能。 一个目标应用程序，特别是相关的普通公民是设计的自动基于内容的图像检索系统。 在这样的系统中，每个图像可以由某个高维（10到20）特征空间中的一个点表示，并且同一类中的图像（例如汽车的图像）聚集在某个流形（例如“汽车流形”）上。 这个流形的形状的描述（它比类中对象的真实形状简单）然后可以用来检索数据库中的所有图像，例如汽车，因为它们在高维空间中的表示将比任何其他流形更接近相应的流形。