Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis最新文献_第2页

Optimal linear transformation for MRI feature extraction MRI特征提取的最优线性变换

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534058

H. Soltanian-Zadeh, J. Windham, D. Peck

引用次数: 75

Contour model guided image warping for medical image interpolation 轮廓模型引导图像翘曲的医学图像插值

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534085

W. Shih, W. Lin, C.-T. Chen

{"title":"Contour model guided image warping for medical image interpolation","authors":"W. Shih, W. Lin, C.-T. Chen","doi":"10.1109/MMBIA.1996.534085","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534085","url":null,"abstract":"An interpolation method using contours of organs as the control parameters is proposed to recover the intensity information in the physical gaps of serial cross-sectional images. In the authors' method, contour models are used for generating the control lines required for the image warping algorithm. Contour information derived from this contour-model-based segmentation process is processed and used as the control parameters to warp the corresponding regions in both input images into compatible shapes. In this way, the reliability of establishing the correspondence among different segments of the same organs is improved and the intensity information for the interpolated intermediate slices can be derived more faithfully. In comparison with the existing intensity interpolation algorithms that only search for corresponding points in a small physical neighborhood, this method provides more meaningful correspondence relationships by warping regions in images into similar shapes before resampling to account for significant shape differences. Experimental results show that this method generates more close to realistic and less blurred interpolated images especially when the shaped difference of corresponding contours is significant.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129050209","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: 2

Hierarchical Bayesian classification of multimodal medical images 多模态医学图像的层次贝叶斯分类

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534057

K. Mardia, T. J. Hainsworth, J. Kirkbride, M. Hurn, E. Berry

引用次数: 1

Incorporating connected region labelling into automated image registration using mutual information 利用互信息将连通区域标记纳入自动图像配准

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534054

Colin Studholme, Derek L. G. Hill, David J. Hawkes

引用次数: 104

A geometric approach to segmentation and analysis of 3D medical images 三维医学图像分割与分析的几何方法

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534076

R. Malladi, Ron Kimmel, D. Adalsteinsson, G. Sapiro, Vicent Caselles, J. Sethian

引用次数: 96

On computing aspect graphs of smooth shapes from volumetric data 从体积数据计算光滑形状的方面图

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534082

J. Noble, D. L. Wilson, J. Ponce

{"title":"On computing aspect graphs of smooth shapes from volumetric data","authors":"J. Noble, D. L. Wilson, J. Ponce","doi":"10.1109/MMBIA.1996.534082","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534082","url":null,"abstract":"The authors address the problem of computing the aspect graph of an object from volumetric image data, with applications in medical image analysis and interpretation. Anatomical surfaces are assumed to be smooth and are identified as the zero set of a three-dimensional density function (e.g., a CT, MR, or ultrasound image). The orthographic-projection aspect graph is constructed by partitioning the view sphere at infinity into maximal regions bounded by visual event curves. These events are the intersections of the view sphere with surfaces ruled by singular tangent lines that graze the object's surface along a set of critical curves. For each visual event the proposed algorithm constructs a new density function from the original one and its derivatives, and computes the corresponding critical curve as the intersection of the object's surface with the zero set of the new density function. Once the critical curves have been traced, the regions of the sphere delineated by the corresponding visual events are constructed through cell decomposition, and a representative aspect is constructed for each region by computing the occluding contour for a sample viewing direction. A preliminary implementation of the proposed approach has been constructed and experiments with synthetic data and real medical data are presented. Extensions to the sectional imaging case are also discussed.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128740147","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: 11

Deformable models in medical image analysis 医学图像分析中的可变形模型

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534069

Akshay K. Singh, Dmitry Goldgof, Demetri Terzopoulos

{"title":"Deformable models in medical image analysis","authors":"Akshay K. Singh, Dmitry Goldgof, Demetri Terzopoulos","doi":"10.1109/MMBIA.1996.534069","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534069","url":null,"abstract":"This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable model are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching, and motion tracking.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115100192","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: 388

Deformable B-solids and implicit snakes for localization and tracking of SPAMM MRI-data 用于定位和跟踪SPAMM mri数据的可变形b实体和隐式蛇

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534071

P. Radeva, A. Amini, Jiantao Huang, E. Martí

{"title":"Deformable B-solids and implicit snakes for localization and tracking of SPAMM MRI-data","authors":"P. Radeva, A. Amini, Jiantao Huang, E. Martí","doi":"10.1109/MMBIA.1996.534071","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534071","url":null,"abstract":"To date, MRI-SPAMM data from different image slices have been analyzed independently. In this paper we propose an approach for 3D tag localization and tracking of SPAMM data by a novel deformable B-solid. The solid is defined in terms of a 3D tensor product B-spline. The iso-parametric curves of the B-spline solid have special importance. These are termed implicit snakes as they deform under image forces from tag lines in different image slices. The localization and tracking of tag lines is performed under constraints of continuity and smoothness of the B-solid. The framework unifies the problems of localization, and displacement fitting and interpolation into the same procedure utilizing B-spline bases for interpolation. To track motion from boundaries and restrict image forces to the myocardium, a volumetric model is employed as a pair of coupled endocardial and epicardial B-spline surfaces. To recover deformations in the LV an energy-minimization problem is posed where both tag and LV boundary data are used. The framework has been implemented on tag data from short axis (SA) cardiac images, as well as SA left ventricle (LV) boundaries, and is currently being extended to include long axis (LA) data.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122465186","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: 28

Intensity ridge and widths for tubular object segmentation and description 用于管状物体分割和描述的强度脊和宽度

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534065

S. Aylward, E. Bullitt, S. Pizer, Dave H. Eberly

{"title":"Intensity ridge and widths for tubular object segmentation and description","authors":"S. Aylward, E. Bullitt, S. Pizer, Dave H. Eberly","doi":"10.1109/MMBIA.1996.534065","DOIUrl":"https://doi.org/10.1109/MMBIA.1996.534065","url":null,"abstract":"Introduces a technique for the automated description of tubular objects in 3D medical images. The goal of automated 3D object description is to extract a representation which consistently details the location, size, and structure of objects in 3D images using minimal user interaction. Such a representation provides a means by which objects can be classified, quantifiably evaluated, and registered. It also serves as a region of interest specification for visualization processes. The technique presented in this paper is suited for generating representations of 3D objects with nearly circular cross sections which have, possibly as a result of a global operation (e.g., blurring), intensity extrema near their centers. Such tubular objects commonly occur within human anatomy (e.g., vessels and selected bones). The medial axis of each of these objects is well approximated by its intensity ridge. The scales of the local maxima in medialness at all points along the ridge can be mapped to local width estimates. Together these measures capture the location, size, and structure of tubular objects. This paper covers the mathematical basis, the implementation issues, and the application of this technique to the extraction of vessels from 3D magnetic resonance angiographic images and bones from 3D X-ray computed tomographic images.","PeriodicalId":436387,"journal":{"name":"Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116429135","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: 162

An integrated approach for surface finding in medical images 医学图像表面发现的集成方法

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis Pub Date : 1996-06-21 DOI: 10.1109/MMBIA.1996.534077

A. Chakraborty, L. Staib, J. Duncan

引用次数: 12