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2013 IEEE 10th International Symposium on Biomedical Imaging最新文献

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Variational variable selection to assess experimental condition relevance in event-related fMRI 变分变量选择评估事件相关fMRI实验条件相关性
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556821
C. Bakhous, F. Forbes, T. Vincent, M. Dojat, P. Ciuciu
{"title":"Variational variable selection to assess experimental condition relevance in event-related fMRI","authors":"C. Bakhous, F. Forbes, T. Vincent, M. Dojat, P. Ciuciu","doi":"10.1109/ISBI.2013.6556821","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556821","url":null,"abstract":"Brain functional exploration investigates the nature of neural processing following cognitive or sensory stimulation. This goal is not fully accounted for in most functional Magnetic Resonance Imaging (fMRI) analysis which usually assumes that all delivered stimuli possibly generate a BOLD response everywhere in the brain although activation is likely to be induced by only some of them in specific brain regions. Generally, criteria are not available to select the relevant conditions or stimulus types (e.g. visual, auditory, etc.) prior to activation detection and the inclusion of irrelevant events may degrade the results, particularly when the Hemodynamic Response Function (HRF) is jointly estimated. To face this issue, we propose an efficient variational procedure that automatically selects the conditions according to the brain activity they elicit. It follows an improved activation detection and local HRF estimation that we illustrate on synthetic and real fMRI data.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123627922","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: 4
Multi-class regularization parameter learning for graph cut image segmentation 图割图像分割的多类正则化参数学习
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556813
S. Candemir, K. Palaniappan, Y. S. Akgul
{"title":"Multi-class regularization parameter learning for graph cut image segmentation","authors":"S. Candemir, K. Palaniappan, Y. S. Akgul","doi":"10.1109/ISBI.2013.6556813","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556813","url":null,"abstract":"One of the first steps of computer-aided systems is robustly detect the anatomical boundaries. Literature has several successful energy minimization based algorithms which are applied to medical images. However, these algorithms depend on parameters which need to be tuned for a meaningful solution. One of the important parameters is the regularization parameter (λ) which is generally estimated in an ad-hoc manner and is used for the whole data set. In this paper we claim that λ can be learned by local features which hold the regional characteristics of the image. We propose a λ estimation system which is modeled as a multi-class classification scheme. We demonstrate the performance of the approach within graph cut segmentation framework via qualitative results on chest X-rays. Experimental results indicate that predicted parameters produce better segmentation results.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991457","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: 36
Improving resolution of fluorescent microscopy using speckle illumination and joint sparse recovery 利用散斑照明和联合稀疏恢复提高荧光显微镜分辨率
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556545
J. C. Ye, Junhong Min, Jaeduck Jang
{"title":"Improving resolution of fluorescent microscopy using speckle illumination and joint sparse recovery","authors":"J. C. Ye, Junhong Min, Jaeduck Jang","doi":"10.1109/ISBI.2013.6556545","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556545","url":null,"abstract":"A variety of far-field super-resolution microscopy techniques have been proposed recently by exploiting nonlinear phenomenon in optics or specialized photo-switchable probes. Here, we present a linear optics based super-resolution microscopy for standard probes and experimental protocols. Rather than exploiting the nonlinearity in optics or photo-switchable probes, we use a nonlinear reconstruction algorithm that exploits joint sparsity of fluorescent emission from multiple random illumination. To maximize the resolution improvement owing to the joint sparsity, spatially incoherent speckle illumination is used for conventional epi-fluorescence microscopy setup. Experimental results obtained from a nano-pattern and bio-samples stained with standard fluorescent probes along with the proposed method demonstrate that a resolution of up to 37nm can be achieved.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119421","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
Connectivity searchlight: A novel approach for MRI information mapping using multivariate connectivity 连接探照灯:一种利用多变量连接进行MRI信息映射的新方法
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556464
Soheil Faridi, J. Richiardi, P. Vuilleumier, D. Ville
{"title":"Connectivity searchlight: A novel approach for MRI information mapping using multivariate connectivity","authors":"Soheil Faridi, J. Richiardi, P. Vuilleumier, D. Ville","doi":"10.1109/ISBI.2013.6556464","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556464","url":null,"abstract":"Brain mapping using magnetic resonance imaging (MRI) is traditionally performed using voxel-wise statistical hypothesis testing. Such mass-univariate approach ignores subtle spatial interactions. The searchlight method, in contrast, uses a multivariate predictive model in each local neighborhood in brain space-named the searchlight. The classification performance is then reported at the center of the searchlight to build an information map. We extend the searchlight technique to take into account additional voxels that can be considered as a meaningful network; i.e., we define a criterion of multivariate connectivity to identify voxels that are statistically dependent on those in searchlight. We coin the term “connectivity searchlight” for the extended searchlight. Using simulated data, we empirically show improved performance for brain regions with low signal-to-noise ratio and recovery of underlying network structures that would otherwise remain hidden. The proposed methodology is general and can be applied to both functional and structural data. We also demonstrate promising results on a well-known fMRI dataset where images of different categories are presented.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126145917","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: 1
Scapula Statistical Shape Model construction based on watershed segmentation and elastic registration 基于分水岭分割和弹性配准的肩胛骨统计形状模型构建
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556422
M. Mayya, S. Poltaretskyi, C. Hamitouche-Djabou, J. Chaoui
{"title":"Scapula Statistical Shape Model construction based on watershed segmentation and elastic registration","authors":"M. Mayya, S. Poltaretskyi, C. Hamitouche-Djabou, J. Chaoui","doi":"10.1109/ISBI.2013.6556422","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556422","url":null,"abstract":"Automated bone segmentation is one of the most challenging problems in medical imaging. The increasingly demanded MR imaging suffers from low contrast and signal-to-noise ratio when it comes to bones. To increase the segmentation robustness, a prior model of the structure could guide the segmentation when explicit information is missing or weakly presented. Statistical Shape Models (SSMs) are efficient examples for such application where a set of dense correspondences between the training samples is to be established. The complexity of the anatomy of the scapula's bone is a real challenge at this level. We present an automated SSM construction approach with an adapted initialization to address the correspondences problem. Our approach is atlas-based where landmarks are matched on each sample using rigid and elastic registration. Our innovation stems from the derivation of a robust SSM based on Watershed segmentation which steers the elastic registration at some critical zones.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604789","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
High resolution cardiac shape registration using Ricci flow 使用Ricci流的高分辨率心脏形状配准
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556518
Mingchen Gao, Rui Shi, Shaoting Zhang, W. Zeng, Z. Qian, X. Gu, Dimitris N. Metaxas, L. Axel
{"title":"High resolution cardiac shape registration using Ricci flow","authors":"Mingchen Gao, Rui Shi, Shaoting Zhang, W. Zeng, Z. Qian, X. Gu, Dimitris N. Metaxas, L. Axel","doi":"10.1109/ISBI.2013.6556518","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556518","url":null,"abstract":"Current CT techniques are able to produce isotropic high resolution CT images (0.5mm). Recent research has revealed that the interior of the left ventricle has complex structures and topology, which has potentially valuable information. However, this makes the matching between models much more challenging. In this paper, we propose a novel method to match two models with non-trivial topology. 3D mesh models are flattened onto a 2D planar surfaces using discrete hyperbolic Ricci flow. Therefore, the 3D matching problem is converted to a much simpler 2D matching problem. We show the performance on the registration of high resolution left ventricle models.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124699100","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
Tract-oriented statistical group comparison of diffusion in sheet-like white matter 张状白质弥散的定向统计学组间比较
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556767
M. Lyksborg, T. Dyrby, P. Sørensen, M. Blinkenberg, H. Siebner, D. Alexander, R. Larsen, Hui Zhang
{"title":"Tract-oriented statistical group comparison of diffusion in sheet-like white matter","authors":"M. Lyksborg, T. Dyrby, P. Sørensen, M. Blinkenberg, H. Siebner, D. Alexander, R. Larsen, Hui Zhang","doi":"10.1109/ISBI.2013.6556767","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556767","url":null,"abstract":"Identifying specific structures of the brain where pathology differs between groups of subjects may aid to develop imaging-based markers for disease diagnosis. We propose a new technique for doing multivariate statistical analysis on white matter tracts with sheet like shapes. Previous works assume tube-like shapes, not always suitable for modelling the white matter tracts of the brain. The tract-oriented technique aimed at group studies, integrates the usage of multivariate features and outputs a single value of significance indicating tract-specific differences. This is in contrast to voxel based analysis techniques which outputs a significance per voxel basis, and requires multiple comparison correction. We demonstrate our technique by comparing a group of controls with a group of Multiple Sclerosis subjects obtaining significant differences on 11 different fascicle structures.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125040550","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
Linear unmixing of hyperspectral images for analysis of fluorescently-labeled cellswith imperfect endmember spectra 用于分析端元光谱不完美的荧光标记细胞的高光谱图像的线性解混
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556440
B. Sirkeci-Mergen, M. Keralapura, Serena Coelho, S. Leavesley, T. Rich
{"title":"Linear unmixing of hyperspectral images for analysis of fluorescently-labeled cellswith imperfect endmember spectra","authors":"B. Sirkeci-Mergen, M. Keralapura, Serena Coelho, S. Leavesley, T. Rich","doi":"10.1109/ISBI.2013.6556440","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556440","url":null,"abstract":"Spectral unmixing is the method of the detecting and localizing subpixel features by estimating the relative concentrations of the reference spectra. For most applications, spectral unmixing methods should account for spectral reference ambiguity, and concentration estimates with non-negativity and sum-to-one constraints. In this paper, we propose total least squares (TLS) based methods for unmixing of hyperspectral images obtained via fluorescence microscopy. Here, we formulate the restricted TLS as a constrained quadratic optimization problem which can be solved efficiently. The performance of restricted TLS is compared to the existing least squares based methods via simulations.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129416976","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: 1
Classification with reject option using contextual information 使用上下文信息进行带有拒绝选项的分类
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556780
Filipe Condessa, J. Bioucas-Dias, C. Castro, J. Ozolek, J. Kovacevic
{"title":"Classification with reject option using contextual information","authors":"Filipe Condessa, J. Bioucas-Dias, C. Castro, J. Ozolek, J. Kovacevic","doi":"10.1109/ISBI.2013.6556780","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556780","url":null,"abstract":"We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance by including local and nonlocal contextual information, at the expense of rejecting a fraction of the samples. As a probabilistic model, we adopt a multinomial logistic regression. We use a discriminative random model for the description of the problem; we introduce reject option into the classification problem through association potential, and contextual information through interaction potential. We validate the method on the images of H&E-stained teratoma tissues and show the increase in the classifier performance when rejecting part of the assigned class labels.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129471909","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: 16
A statistical analysis of spatial colocalization using Ripley's K function 基于Ripley K函数的空间共定位统计分析
2013 IEEE 10th International Symposium on Biomedical Imaging Pub Date : 2013-04-07 DOI: 10.1109/ISBI.2013.6556620
T. Lagache, V. Meas-Yedid, J. Olivo-Marin
{"title":"A statistical analysis of spatial colocalization using Ripley's K function","authors":"T. Lagache, V. Meas-Yedid, J. Olivo-Marin","doi":"10.1109/ISBI.2013.6556620","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556620","url":null,"abstract":"The author of this work present an appendix paper entitled: \"A statistical analysis of spatial colocalization using Ripley's K function.\" Thibault Lagache *, Vannary Meas-Yedid, lean-Christophe Olivo-Marin Institut Pasteur, Quantitative Image Analysis Unit, F-75015 Paris, France CNRS URA 2582, F-75015 Paris, France","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484738","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: 24
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