Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)最新文献_第2页

Maximum a posteriori segmentation for medical visualization 医学可视化的最大后验分割

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692402

L. Hibbard

{"title":"Maximum a posteriori segmentation for medical visualization","authors":"L. Hibbard","doi":"10.1109/BIA.1998.692402","DOIUrl":"https://doi.org/10.1109/BIA.1998.692402","url":null,"abstract":"This is a practical contouring method combining region growing, gradient edge detection, and prior shape constraints to compute contours throughout a three dimensional, computed tomography image dataset. Beginning with a sample of known object interior pixels, alternating steps of incremental region growth are followed by determination of an optimal contour, fitted simultaneously to the current region's perimeter local maxima in the gray level gradient, and to the shapes of prior contours of the object. The resulting contour corresponds to the maximum over all the iteratively-computed contours. Region growing is conducted by a supervised classifier developed on the fly for each object-section. Contours are parametric curves where the parameters are the independent variables of an objective function. The parameters also are treated as random variables whose distributions constrain future contour shapes. Both the region growing and the boundary finding are posed as maximum a posteriori problems. The method propagates contours from section to section using the texture classifier region template, and parametric shape prior probabilities from a previous section's contour to begin contour determination on a succeeding section. Initially intended as a drawing tool to speed-up interactive contouring on CT images in radiation therapy planning, the method is fully competent to run automatically as long as initial object-interior samples are provided.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133558491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Experimental results of a vision-based burn scar assessment technique 基于视觉的烧伤疤痕评估技术的实验结果

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692455

L. Tsap, Dmitry Goldgof, S. Sarkar, Pauline S. Powers

引用次数: 9

Quantitative study of brain anatomy 脑解剖定量研究

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692398

Mei Chen, T. Kanade, H. Rowley, D. Pomerleau

引用次数: 13

Model based multiscale detection of 3D vessels 基于模型的三维血管多尺度检测

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692517

K. Krissian, G. Malandain, N. Ayache, Régis Vaillant, Y. Trousset

引用次数: 18

A fast algorithm for generating large tetrahedral 3D finite element meshes from magnetic resonance tomograms 从磁共振层析图生成大型四面体三维有限元网格的快速算法

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692451

Ulrich Hartmann, F. Kruggel

{"title":"A fast algorithm for generating large tetrahedral 3D finite element meshes from magnetic resonance tomograms","authors":"Ulrich Hartmann, F. Kruggel","doi":"10.1109/BIA.1998.692451","DOIUrl":"https://doi.org/10.1109/BIA.1998.692451","url":null,"abstract":"Addresses the problem of generating three-dimensional (3D) finite element (FE) meshes from medical voxel datasets. With their background in cognitive neuroscience, the authors deal with brain MR tomograms of up to 256/sup 3/ voxels which contain a multitude of incompletely definable, complex-shaped objects. The authors describe an algorithm that allows the fast and stable creation of very large 3D meshes with well-defined geometric properties. The task of generating anisotropic meshes consisting of up to one million tetrahedra is fulfilled within minutes on a standard workstation. As the angles of the tetrahedra have a direct influence on the stability of the finite element analysis, special care has been taken to assess the element quality. The authors' algorithm is based on the idea of an image-based spatial decomposition of the problem domain yielding smaller subproblems that can efficiently be handled. The authors' primary purpose is to set up mechanical and electro-magnetical finite element models of the brain. However, their FE meshes could also be useful in other types of finite element analyses or as deformable volume models for shape descriptions and shape comparisons.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124575371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

Constrained thin-plate spline reconstruction of 2D deformations from tagged MRI 标记MRI二维变形的约束薄板样条重建

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692389

A. Amini, Yasheng Chen, J. Sun, V. Mani

引用次数: 1

Three-dimensional model-based segmentation of brain MRI 基于三维模型的脑MRI分割

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692374

A. Kelemen, Gábor Székely, Guido Gerig

{"title":"Three-dimensional model-based segmentation of brain MRI","authors":"A. Kelemen, Gábor Székely, Guido Gerig","doi":"10.1109/BIA.1998.692374","DOIUrl":"https://doi.org/10.1109/BIA.1998.692374","url":null,"abstract":"This paper presents a new technique for the automatic model-based segmentation of 3-D objects from volumetric image data. The development closely follows the seminal work of Cootes et al. (1994) but presents various new solutions to come up with a true 3-D technique rather than a slice-by-slice 2-D processing. The segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of models. Geometric models are derived front a sample set of image data which have been segmented by experts. The surfaces of these binary objects are converted into a parametric surface net which is normalized to get an invariant object-centered coordinate system. Surface descriptions are expanded into series of spherical harmonics which provide parametric representations of object shapes. Gray-level information is represented by 1-D profiles normal to the surface. The alignment is based on the well-accepted stereotactic coordinate system since the driving application is the segmentation of brain objects. Shape statistics are calculated from the parametric shape representations rather than from the spatial coordinates of sets of points. After initializing the mean shape in a new data set on the basis of the alignment coordinates, the model elastically deforms in accordance to displacement forces across the surface but is restricted only by shape deformation constraints. The technique has been applied to segment left and right hippocampal structures from a large series of 3-D magnetic resonance scans taken from a schizophrenia study.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134157512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 69

Intensity-invariant 2D+T acoustic boundary detection 强度不变二维+T声学边界检测

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692423

M. Mulet-Parada, J. A. Noble

引用次数: 5

Consistency and stability of active contours with Euclidean and non-Euclidean arc-lengths 欧几里得弧长和非欧几里得弧长活动轮廓的一致性和稳定性

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692413

Tianyun Ma, H. Tagare

引用次数: 2

Matching point features using mutual information 利用互信息匹配点特征

Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162) Pub Date : 1998-06-26 DOI: 10.1109/BIA.1998.692445

A. Rangarajan, J. S. Duncan

引用次数: 15