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2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)最新文献

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Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search 利用遗传算法和非盲搜索重建电阻抗断层成像
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867832
R. R. Ribeiro, A. R. S. Feitosa, R. E. D. Souza, W. Santos
{"title":"Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search","authors":"R. R. Ribeiro, A. R. S. Feitosa, R. E. D. Souza, W. Santos","doi":"10.1109/ISBI.2014.6867832","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867832","url":null,"abstract":"The development and improvement of non-invasive imaging techniques have been increasing in the last decades, due to interests from both academy and industry. Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using genetic algorithms employing elitist strategies. The initial set of solutions used by the elitist genetic algorithm includes a noisy version of the solution obtained from the backprojection algorithm, according to Saha and Bandyopadhyay's criterion for non-blind initial search in optimization algorithms, in order to accelerate convergence and improve performance.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124990661","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: 19
Renal cortex localization by combining 3D Generalized Hough Transform and 3D Active Appearance Models 结合三维广义霍夫变换和三维活动外观模型的肾皮质定位
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6868109
Chao Jin, Dehui Xiang, Xinjian Chen
{"title":"Renal cortex localization by combining 3D Generalized Hough Transform and 3D Active Appearance Models","authors":"Chao Jin, Dehui Xiang, Xinjian Chen","doi":"10.1109/ISBI.2014.6868109","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868109","url":null,"abstract":"Automatic localization is one of important steps in medical image segmentation. In this paper, a model-based method for three-dimensional image localization is developed. Our method is based on a combination of 3D Generalized Hough Transform and 3D Active Appearance Models. It consists of two main parts: training and localization. The proposed method was tested on a clinical abdomen CT data set, including 27 contrast-enhanced volume data, in which 15 were chose as training data while the other 12 as testing data. The experimental results show that: (1) an overall cortex localization average distance is 12.58±3.26 voxels. (2) The proposed method is highly efficient, the running time is about only 35.70±3.62 seconds for each volume data.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151460","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
Automatic simultaneous segmentation and fast registration of histological images 组织图像的自动同步分割和快速配准
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867985
J. Kybic, Jiri Borovec
{"title":"Automatic simultaneous segmentation and fast registration of histological images","authors":"J. Kybic, Jiri Borovec","doi":"10.1109/ISBI.2014.6867985","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867985","url":null,"abstract":"We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mutual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and robustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appearance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127668102","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: 12
SEMI-automated liver CT segmentation using Laplacian meshes 基于拉普拉斯网格的半自动肝脏CT分割
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867952
G. Chartrand, T. Cresson, R. Chav, A. Gotra, A. Tang, J. Guise
{"title":"SEMI-automated liver CT segmentation using Laplacian meshes","authors":"G. Chartrand, T. Cresson, R. Chav, A. Gotra, A. Tang, J. Guise","doi":"10.1109/ISBI.2014.6867952","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867952","url":null,"abstract":"Liver volumetry is considered to be an accurate indicator of hepatic function and a prognostic indicator in hepatic surgery planning. Despite many years of research, automated liver segmentation remains an open challenge and manual segmentation is still widely used clinically although it is time-consuming and tedious. In this paper we propose a novel semi-automated segmentation method based on deformable models independent of training data. First, an initial shape of the liver is generated by variational interpolation from a few user-generated contours. A template-matching method then identifies target points corresponding to the liver boundary. Using a Laplacian mesh optimization framework, the geometric model is iteratively deformed until it converges to the liver boundary. This liver segmentation method was tested against 20 publicly available datasets and is shown to be fast and robust to pathological cases with a mean volumetric overlap error of 6.8% and an average runtime under 6 minutes.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990724","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
T2 prime mapping from highly undersampled data using compressed sensing with patch based low rank penalty 利用基于补丁的低秩惩罚压缩感知从高度欠采样数据中映射T2素数
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867953
Dongwook Lee, E. Kim, Huisu Yoon, Sunghong Park, J. C. Ye
{"title":"T2 prime mapping from highly undersampled data using compressed sensing with patch based low rank penalty","authors":"Dongwook Lee, E. Kim, Huisu Yoon, Sunghong Park, J. C. Ye","doi":"10.1109/ISBI.2014.6867953","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867953","url":null,"abstract":"In magnetic resonance (MR) imaging, T2 and T2 star (T2*) relaxation times represent tissue properties, which can be quantified by specific imaging sequences. Especially, T2 prime (T2') that can be derived from T2 and T2* are clinically valuable for delineation of areas with increased oxygen extraction fraction in acute stroke. However, there are limitations in this method because it requires acquisition of many images for the generation of T2 and T2* relaxation time maps. In particular, time saving is the most important factor in acquisition of MRI in acute ischemic stroke because therapy should be given to patients as soon as possible. Therefore, to reduce the acquisition time of MR data, we use a compressed sensing algorithm using patch based low rank penalty for the reconstruction of T2 and T2* weighted images to obtain the T2 prime map. Our results showed that significant acceleration in T2' image acquisition is possible using the proposed method.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116327607","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
Joint segmentation of right and left cardiac ventricles using multi-label graph cut 利用多标签图割对左、右心室进行联合分割
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867900
Damien Grosgeorge, C. Petitjean, S. Ruan
{"title":"Joint segmentation of right and left cardiac ventricles using multi-label graph cut","authors":"Damien Grosgeorge, C. Petitjean, S. Ruan","doi":"10.1109/ISBI.2014.6867900","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867900","url":null,"abstract":"Segmenting the left ventricle (LV) and the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. In particular, the segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a fully automatic segmentation method based on multi-label graph cuts, that makes use of a probabilistic shape model. The shape model is obtained by merging several atlases after their non-rigid registration on the unseen image. This prior is then incorporated into the multi-label graph cut framework in order to guide the segmentation. Our automatic segmentation method has been applied on 754 MR images. We show that encouraging results can be obtained for this challenging application.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126887324","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
TOP-level designs of a hybrid low field MRI-CT system for pulmonary imaging 混合低场MRI-CT肺部成像系统顶层设计
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6868035
Venkata R. Yelleswarapu, Fenglin Liu, W. Cong, Ge Wang
{"title":"TOP-level designs of a hybrid low field MRI-CT system for pulmonary imaging","authors":"Venkata R. Yelleswarapu, Fenglin Liu, W. Cong, Ge Wang","doi":"10.1109/ISBI.2014.6868035","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868035","url":null,"abstract":"We previously discussed “omni-tomography”, but intrinsic conflicts between the magnetic fields of the MRI and the x-ray tube within the CT are inherent. We propose that by using low-field MRI with a negligible fringe field at the site of the CT source, it is possible to create a CT-MRI system with minimal interference. Low field MRI is particularly useful for lung imaging, where hyperpolarized gas can enhance the signal. Three major designs were considered and simulated, with modifications in coil design and axis allowing for further variation. The first uses Halbach arrays to minimize magnetic fields outside, the second uses solenoids pairs with active shielding, and the third uses a rotating compact MRI-CT. Each system is low field, which may allow the implementation of a standard rotating CT. Both structural and functional information can be acquired simultaneously for a true hybrid image with matching temporal and spatial image acquisition.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250715","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
Successive convex optimization for point set registration 点集配准的连续凸优化
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867989
Yi Gao
{"title":"Successive convex optimization for point set registration","authors":"Yi Gao","doi":"10.1109/ISBI.2014.6867989","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867989","url":null,"abstract":"We propose a new robust point set registration technique utilizing the global information of the two sets, rather than looking locally, for constructing the point-wise correspondence and estimating the transformation in between. Then, a multi-scale scheme further extends the algorithm to handling nonlinear diffeomorphic transformations. The algorithm is tested on 3D geometric and protein structure data sets to demonstrate its robustness to severe distortions.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126075422","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
Fast algorithm for estimating the regional mechanical function of the left ventricle from 4D cardiac CT data 基于4D心脏CT数据的左心室区域力学功能快速估计算法
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867903
Yechiel Lamash, A. Fischer, J. Lessick
{"title":"Fast algorithm for estimating the regional mechanical function of the left ventricle from 4D cardiac CT data","authors":"Yechiel Lamash, A. Fischer, J. Lessick","doi":"10.1109/ISBI.2014.6867903","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867903","url":null,"abstract":"Cardiac pathologies are generally associated with regional ventricular dysfunction. Methods for estimating the regional myocardial motion from cardiac CT image data generally ignore the rotational velocities. Reasons for this include the challenges of sparse image deformation clues, low SNR and the low temporal resolution. In the current study we propose a fast algorithm for evaluating the mechanical function of the left ventricle from cardiac CT data. A compact parametric motion model is used to describe the regional 3D contraction and twist. The algorithm is based on regularized multi-homography image registration. The rotational velocities are estimated and compared to their respective values in the literature, with good agreement. Good performance in classifying the segments as normal or abnormal with respect to expert's visual scores is obtained.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475609","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 with a shape dictionary 用形状字典分割
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2014-07-31 DOI: 10.1109/ISBI.2014.6867882
Wenyang Liu, D. Ruan
{"title":"Segmentation with a shape dictionary","authors":"Wenyang Liu, D. Ruan","doi":"10.1109/ISBI.2014.6867882","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867882","url":null,"abstract":"Image segmentation plays an important role in many medical applications. Automatic segmentation algorithms are challenged by low SNR and significant artifacts resulting from motion and signal voids. In this study, we propose a novel level set based segmentation method with a shape dictionary. Unlike previous studies that use a single template or probabilistic models, we propose to construct a shape dictionary and model the shape prior as sparse combinations of shape templates in the dictionary. The proposed method generated promising segmentation results on low SNR MR images, even with signal voids.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273691","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: 3
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