发布求助
文献互助 智能选刊 最新文献

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro最新文献

筛选
英文 中文
Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers 使用局部贝叶斯分类器的显微图像细胞分割
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490399
Zhaozheng Yin, Ryoma Bise, Mei Chen, T. Kanade
{"title":"Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers","authors":"Zhaozheng Yin, Ryoma Bise, Mei Chen, T. Kanade","doi":"10.1109/ISBI.2010.5490399","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490399","url":null,"abstract":"Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748850","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: 81
Assessing tumour vascularity with 3D contrast-enhanced ultrasound: A new semi-automated segmentation framework 用3D增强超声评估肿瘤血管:一种新的半自动分割框架
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490351
A. Gasnier, R. Ardon, C. Ciofolo-Veit, E. Leen, J. Correas
{"title":"Assessing tumour vascularity with 3D contrast-enhanced ultrasound: A new semi-automated segmentation framework","authors":"A. Gasnier, R. Ardon, C. Ciofolo-Veit, E. Leen, J. Correas","doi":"10.1109/ISBI.2010.5490351","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490351","url":null,"abstract":"3D contrast-enhanced ultrasound (CEUS) is a powerful imaging technique for tumour vascularity assessment, which is critical for radio-frequency ablation (RFA) planning or for the assessment of response to antiangiogenic therapies. In this paper, we propose a novel semi-automated method for the quantification of tumour vascularity in 3D CEUS data. We apply a two-step framework combining an interactive segmentation of the tumour necrosis followed by an automatic detection of the vascularity based on implicit representations. Experimental results on 3D CEUS images of renal cell carcinomas (RCC) show that our method is promising in terms of speed and quality.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751118","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: 10
Automated measurement and segmentation of abdominal adipose tissue in MRI MRI中腹部脂肪组织的自动测量和分割
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490141
D. Sussman, Jianhua Yao, R. Summers
{"title":"Automated measurement and segmentation of abdominal adipose tissue in MRI","authors":"D. Sussman, Jianhua Yao, R. Summers","doi":"10.1109/ISBI.2010.5490141","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490141","url":null,"abstract":"Obesity has become widespread in America and has been identified as a risk factor for many illnesses. Measuring adipose tissue (AT) with traditional means is often unreliable and inaccurate. MRI provides a safe and minimally invasive means to measure AT accurately and segment visceral AT from subcutaneous AT. However, MRI is often corrupted by image artifacts which make manual measurements difficult and time consuming. We present a fully automated method to measure and segment abdominal AT in MRI. Our method uses non-parametric non-uniform intensity normalization (N3) to correct for image artifacts and inhomogeneities, fuzzy c-means to cluster AT regions and active contour models to separate subcutaneous and visceral AT. Our method was able to measure images with severe intensity inhomogeneities and demonstrated agreement with two manual users that was close to the agreement between the manual users.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133278747","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: 8
Static and dynamic cardiac modelling: Initial strides and results towards a quantitatively accurate mechanical heart model 静态和动态心脏建模:朝着定量准确的机械心脏模型的初步进展和结果
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490300
C. Constantinides, N. Aristokleous, G. Johnson, Dimitris Perperides
{"title":"Static and dynamic cardiac modelling: Initial strides and results towards a quantitatively accurate mechanical heart model","authors":"C. Constantinides, N. Aristokleous, G. Johnson, Dimitris Perperides","doi":"10.1109/ISBI.2010.5490300","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490300","url":null,"abstract":"Magnetic Resonance Imaging (MRI) has exhibited significant potential for quantifying cardiac function and dysfunction in the mouse. Recent advances in high-resolution cardiac MR imaging techniques have contributed to the development of acquisition approaches that allow fast and accurate description of anatomic structures, and accurate surface and finite element (FE) mesh model constructions for study of global mechanical function in normal and transgenic mice. This study presents work in progress for construction of quantitatively accurate three-dimensional (3D) and 4D dynamic surface and FE models of murine left ventricular (LV) muscle in C57BL/6J (n=10) mice. Constructed models are subsequently imported into commercial software packages for the solution of the constitutive equations that characterize mechanical function, including computation of the stress and strain fields. They are further used with solid-free form fabrication processes to construct model-based material renditions of the human and mouse hearts.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131929169","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: 6
Inference of functional connectivity from structural brain connectivity 从脑结构连通性推断功能连通性
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490188
F. Deligianni, E. Robinson, C. Beckmann, D. Sharp, A. Edwards, D. Rueckert
{"title":"Inference of functional connectivity from structural brain connectivity","authors":"F. Deligianni, E. Robinson, C. Beckmann, D. Sharp, A. Edwards, D. Rueckert","doi":"10.1109/ISBI.2010.5490188","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490188","url":null,"abstract":"Studies that examine the relationship of functional and structural connectivity are tremendously important in interpreting neurophysiological data. Although, the relationship between functional and structural connectivity has been explored with a number of statistical tools [1, 2], there is no explicit attempt to quantitatively measure how well functional data can be predicted from structural data. Here, we predict functional connectivity from structural connectivity, explicitly, by utilizing a predictive model based on PCA and CCA. The combination of these techniques allowed the reduction of dimensionality and modeling of inter-correlations, successfully. We provide both qualitative and quantitative results based on a leave-one-out validation.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100696","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: 13
Motion-based, multi-modality image registration for cardiac imaging 心脏成像中基于运动的多模态图像配准
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490299
A. Cebula, D. Gilland, J. Parker, Yunmei Chen
{"title":"Motion-based, multi-modality image registration for cardiac imaging","authors":"A. Cebula, D. Gilland, J. Parker, Yunmei Chen","doi":"10.1109/ISBI.2010.5490299","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490299","url":null,"abstract":"A novel image registration method is presented for multi-modality, gated cardiac imaging. The motion of the myocardium is registered instead of attributes obtained from image intensities, which may be drastically different. Optical flow methods are used to estimate a set of 3D vector fields for both modalities. The 3D vector fields are assumed to be similar and are rigidly aligned by minimizing a sum-of-squares error objective function. Evaluation of the motion-based (MB) method was performed using simulated cardiac SPECT and CT images of a 4D thorax phantom for registration errors of 1 to 3 cm translation, with and without rotation. The MB method was compared to a mutual information (MI) based method. The MB method was able to register the images with an accuracy of 1–5 mm for an anatomical point in the left ventricle. The MI method required a common background distribution within the two modalities for accurate registration.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123856031","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: 0
Segmentation of 2D stress echocardiography sequences using rest-based patient-specific prior information 利用基于休息的患者特异性先验信息分割二维应激超声心动图序列
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490348
Adeala Zabair, J. A. Noble
{"title":"Segmentation of 2D stress echocardiography sequences using rest-based patient-specific prior information","authors":"Adeala Zabair, J. A. Noble","doi":"10.1109/ISBI.2010.5490348","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490348","url":null,"abstract":"In stress echocardiography, the heart is imaged at rest and again when stressed to observe the change in function between these two states; the idea being that abnormalities will be exaggerated and therefore easier to identify in stress, but importantly this is referenced to the rest state. Despite the development of segmentation and tracking techniques for the heart at rest, there is little literature on the same for the stressed heart [1]. First we propose a patient-specific segmentation technique that gives a prediction of stress dataset segmentation given rest dataset segmentation for a healthy heart through the use of a global motion model based on Canonical Correlation Analysis (CCA). Secondly, we refine this prior segmentation using texture measures from the rest dataset as reference parameters for maximum likelihood estimation of the boundary in the stress dataset. Results show that for 52 out of 78 datasets, our model gives better results than using the technique described in [2].","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124426157","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: 0
Conditional integration as a way of measuring mediated interactions between large-scale brain networks in functional MRI 条件整合作为一种测量大尺度脑网络间中介相互作用的方法
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490092
D. Coynel, G. Marrelec, V. Perlbarg, J. Doyon, H. Benali
{"title":"Conditional integration as a way of measuring mediated interactions between large-scale brain networks in functional MRI","authors":"D. Coynel, G. Marrelec, V. Perlbarg, J. Doyon, H. Benali","doi":"10.1109/ISBI.2010.5490092","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490092","url":null,"abstract":"Brain regions are thought to be organized in large-scale networks, and studying interactions within and between such networks using functional magnetic resonance imaging (fMRI) could prove relevant for understanding brain's functional organization. Such interactions can be quantified by looking at their integration, a generalized measure of correlation. However, such a measure of integration cannot distinguish between mediated and direct interactions. In this paper, we introduce the concept of conditional integration, in order to provide an index of mediated interactions between networks. We first define conditional integration, and then apply it to both simulated and real fMRI datasets. In both cases results show that mediated interactions can be identified, demonstrating the contribution of conditional integration in functional connectivity studies.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841490","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
Spatiotemporal imaging with partially separable functions: A matrix recovery approach 具有部分可分离功能的时空成像:一种矩阵恢复方法
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490076
J. Haldar, Zhi-Pei Liang
{"title":"Spatiotemporal imaging with partially separable functions: A matrix recovery approach","authors":"J. Haldar, Zhi-Pei Liang","doi":"10.1109/ISBI.2010.5490076","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490076","url":null,"abstract":"There has been significant recent interest in fast imaging with sparse sampling. Conventional imaging methods are based on Shannon-Nyquist sampling theory. As such, the number of required samples often increases exponentially with the dimensionality of the image, which limits achievable resolution in high-dimensional scenarios. The partially-separable function (PSF) model has previously been proposed to enable sparse data sampling in this context. Existing methods to leverage PSF structure utilize tailored data sampling strategies, which enable a specialized two-step reconstruction procedure. This work formulates the PSF reconstruction problem using the matrix-recovery framework. The explicit matrix formulation provides new opportunities for data acquisition and image reconstruction with rank constraints. Theoretical results from the emerging field of low-rank matrix recovery (which generalizes theory from sparse-vector recovery) and our empirical results illustrate the potential of this new approach.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"411-414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127872985","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: 187
A unifying model of perfusion and motion applied to reconstruction of sparsely sampled free-breathing myocardial perfusion MRI 灌注与运动统一模型在稀疏采样自由呼吸心肌灌注MRI重建中的应用
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490065
H. Pedersen, H. Ólafsdóttir, R. Larsen, H. Larsson
{"title":"A unifying model of perfusion and motion applied to reconstruction of sparsely sampled free-breathing myocardial perfusion MRI","authors":"H. Pedersen, H. Ólafsdóttir, R. Larsen, H. Larsson","doi":"10.1109/ISBI.2010.5490065","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490065","url":null,"abstract":"The clinical potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is currently limited by respiratory induced motion of the heart. This paper presents a unifying model of perfusion and motion in which respiratory motion becomes an integral part of myocardial perfusion quantification. Hence, the need for tedious manual motion correction prior to perfusion quantification is avoided. In addition, we demonstrate that the proposed framework facilitates the process of reconstructing DCE-MRI from sparsely sampled data in the presence of respiratory motion. The paper focuses primarily on the underlying theory of the proposed framework, but shows in vivo results of respiratory motion correction and simulation results of reconstructing sparsely sampled data.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713929","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: 8
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信