Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193219
J. Dauguet, A. Hérard, J. Declerck, T. Delzescaux
It is implicitly assumed in most non-rigid registration methods that either corresponding structures can be found in both images, or that the regularization associated with the class of transformations chosen produce pertinent deformations elsewhere. However, when the images to register have a different contrast and resolution (e.g. in vivo / post mortem), or to minimize the deformation of some specific tissues (e.g. tumor, bones), it is necessary to have local control on the displacement field in these regions. We propose in this work an original registration method based on cubic B-spline transformations which allows to bound the variation of volume induced by the estimated transformation (variation of the determinant of the Jacobian of the transformation relatively to 1) in a predefined region. Lagrange multipliers are used to perform the constrained minimization of mutual information. We tested our method on artificial images with different tolerances on the volume variation and on one real 3D image: it performed efficiently while respecting the constraints.
{"title":"Locally constrained cubic B-spline deformations to control volume variations","authors":"J. Dauguet, A. Hérard, J. Declerck, T. Delzescaux","doi":"10.1109/ISBI.2009.5193219","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193219","url":null,"abstract":"It is implicitly assumed in most non-rigid registration methods that either corresponding structures can be found in both images, or that the regularization associated with the class of transformations chosen produce pertinent deformations elsewhere. However, when the images to register have a different contrast and resolution (e.g. in vivo / post mortem), or to minimize the deformation of some specific tissues (e.g. tumor, bones), it is necessary to have local control on the displacement field in these regions. We propose in this work an original registration method based on cubic B-spline transformations which allows to bound the variation of volume induced by the estimated transformation (variation of the determinant of the Jacobian of the transformation relatively to 1) in a predefined region. Lagrange multipliers are used to perform the constrained minimization of mutual information. We tested our method on artificial images with different tolerances on the volume variation and on one real 3D image: it performed efficiently while respecting the constraints.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121512434","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192996
M. Chung, Yu-Chien Wu, A. Alexander
Various cortical measures such as cortical thickness are routinely computed along the vertices of cortical surface meshes. These metrics are used in surface-based morphometric studies. If one wishes to compare the surface-based morphometric studies to 3D volume-based studies at a voxel level, 3D interpolation of the sparsely sampled 2D cortical data is needed. In this paper, we have developed a new computational framework for explicitly representing sparsely sampled cortical data as a linear combination of eigenfunctions of the 3D Laplacian. The eigenfunctions are expressed as the product of spherical Bessel functions and spherical harmonics. The coefficients of the expansion are estimated in the least squares fashion iteratively by breaking the problem into smaller subproblems to reduce a computational bottleneck.
{"title":"3D eigenfunction expansion of sparsely sampled 2D cortical data","authors":"M. Chung, Yu-Chien Wu, A. Alexander","doi":"10.1109/ISBI.2009.5192996","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192996","url":null,"abstract":"Various cortical measures such as cortical thickness are routinely computed along the vertices of cortical surface meshes. These metrics are used in surface-based morphometric studies. If one wishes to compare the surface-based morphometric studies to 3D volume-based studies at a voxel level, 3D interpolation of the sparsely sampled 2D cortical data is needed. In this paper, we have developed a new computational framework for explicitly representing sparsely sampled cortical data as a linear combination of eigenfunctions of the 3D Laplacian. The eigenfunctions are expressed as the product of spherical Bessel functions and spherical harmonics. The coefficients of the expansion are estimated in the least squares fashion iteratively by breaking the problem into smaller subproblems to reduce a computational bottleneck.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114773743","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193007
N. Ducros, A. Silva, J. Dinten, C. Seelamantula, M. Unser, F. Peyrin
This paper deals with the problem of time-resolved fluorescence diffuse optical tomography. We propose a new reconstruction scheme based on a multi-resolution approximation of the time-resolved signals. The underlying basis functions are exponential B-splines that are matched to the decay of fluorescence signals. We illustrate the applicability of the method on phantom data.
{"title":"Time resolved fluorescence diffuse optical tomography using multi-resolution exponential B-splines","authors":"N. Ducros, A. Silva, J. Dinten, C. Seelamantula, M. Unser, F. Peyrin","doi":"10.1109/ISBI.2009.5193007","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193007","url":null,"abstract":"This paper deals with the problem of time-resolved fluorescence diffuse optical tomography. We propose a new reconstruction scheme based on a multi-resolution approximation of the time-resolved signals. The underlying basis functions are exponential B-splines that are matched to the decay of fluorescence signals. We illustrate the applicability of the method on phantom data.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645804","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193220
M. Wildeman, M. Baiker, J. Reiber, C. Löwik, M. Reinders, B. Lelieveldt
In this work we present a method for registration of a CT-derived mouse skin surface to two or more 2D, geometrically calibrated, photographs of the same animal using a similarity transformation model. We show that by using a 3D distance map, which is reconstructed from the animal skin silhouettes in the 2D photographs, and by penalizing large angle differences between distance map gradients and CT-based skin surface normals, we are able to construct a registration criterion that is robust to silhouette outliers and yields accurate results for synthetic and real data (mean skin surface distance 0.12mm and 1.35mm respectively).
{"title":"2D/3D registration of micro-CT data to multi-view photographs based on a 3D distance map","authors":"M. Wildeman, M. Baiker, J. Reiber, C. Löwik, M. Reinders, B. Lelieveldt","doi":"10.1109/ISBI.2009.5193220","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193220","url":null,"abstract":"In this work we present a method for registration of a CT-derived mouse skin surface to two or more 2D, geometrically calibrated, photographs of the same animal using a similarity transformation model. We show that by using a 3D distance map, which is reconstructed from the animal skin silhouettes in the 2D photographs, and by penalizing large angle differences between distance map gradients and CT-based skin surface normals, we are able to construct a registration criterion that is robust to silhouette outliers and yields accurate results for synthetic and real data (mean skin surface distance 0.12mm and 1.35mm respectively).","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127978430","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192991
M. Mescam, M. Kretowski, J. Bézy-Wendling
The use of quantitative imaging for the characterization of hepatic tumors in MRI can improve the diagnosis and therefore the treatment. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. In particular, the lesion arterialization is prominent in the resulting contrast between normal and tumoral tissues in contrast-enhanced images. In order to identify this influence, we propose a multiscale model of liver dynamic contrast-enhanced MRI, consisting of a model of the organ coupled with a model of the image acquisition. A sensitivity analysis of the model to the arterial flow has enabled us to emphasize the existence of relationships between texture parameters in simulated arterial-phase MR images, and the arterialization phenomena involved in carcinogenesis.
{"title":"Texture-based characterization of arterialization in simulated MRI of hypervascularized liver tumors","authors":"M. Mescam, M. Kretowski, J. Bézy-Wendling","doi":"10.1109/ISBI.2009.5192991","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192991","url":null,"abstract":"The use of quantitative imaging for the characterization of hepatic tumors in MRI can improve the diagnosis and therefore the treatment. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. In particular, the lesion arterialization is prominent in the resulting contrast between normal and tumoral tissues in contrast-enhanced images. In order to identify this influence, we propose a multiscale model of liver dynamic contrast-enhanced MRI, consisting of a model of the organ coupled with a model of the image acquisition. A sensitivity analysis of the model to the arterial flow has enabled us to emphasize the existence of relationships between texture parameters in simulated arterial-phase MR images, and the arterialization phenomena involved in carcinogenesis.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121618435","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193111
C. Aguerrebere, P. Sprechmann, P. Musé, R. Ferrando
In refractory epilepsy, the goal of neuroimaging is to localize the region of seizure onset. Tracers that accumulate and remain fixed proportional to regional cerebral blood flow (rCBF) at the time of injection are used to obtain SPECT images of the brain activity during and between seizures. The most used technique for detecting the epileptogenic zone (EZ) is to threshold the co-registered and normalized subtraction of these two images. This method has proven to be very useful but has some disadvantages: result depends on the selected threshold and abundance of false detections. In this paper we propose an a-contrario algorithm for detecting regions of the brain with significant changes in the rCBF using two SPECT images. This new method arises from formal deduction and no arbitrary parameters are involved. Comparisons of both methodologies on six patients are presented. The proposed algorithm shows good results in all cases and is more robust than the thresholding method.
{"title":"A-contrario localization of epileptogenic zones in SPECT images","authors":"C. Aguerrebere, P. Sprechmann, P. Musé, R. Ferrando","doi":"10.1109/ISBI.2009.5193111","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193111","url":null,"abstract":"In refractory epilepsy, the goal of neuroimaging is to localize the region of seizure onset. Tracers that accumulate and remain fixed proportional to regional cerebral blood flow (rCBF) at the time of injection are used to obtain SPECT images of the brain activity during and between seizures. The most used technique for detecting the epileptogenic zone (EZ) is to threshold the co-registered and normalized subtraction of these two images. This method has proven to be very useful but has some disadvantages: result depends on the selected threshold and abundance of false detections. In this paper we propose an a-contrario algorithm for detecting regions of the brain with significant changes in the rCBF using two SPECT images. This new method arises from formal deduction and no arbitrary parameters are involved. Comparisons of both methodologies on six patients are presented. The proposed algorithm shows good results in all cases and is more robust than the thresholding method.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121748678","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193045
Huei-Fang Yang, Y. Choe
Understanding neural connectivity and structures in the brain requires detailed 3D anatomical models, and such an understanding is essential to the study of the nervous system. However, the reconstruction of 3D models from a large set of dense nanoscale medical images is very challenging, due to the imperfections in staining and noise in the imaging process. Manual segmentation in 2D followed by tracking the 2D contours through cross-sections to build 3D structures can be a solution, but it is impractical. In this paper, we propose an automated tracking and segmentation framework to extract 2D contours and to trace them through the z direction. The segmentation is posed as an energy minimization problem and solved via graph cuts. The energy function to be minimized contains a regional term and a boundary term. The regional term is defined over the flux of the gradient vector fields and the distance function. Our main idea is that the distance function should carry the information of the segmentation from the previous image based on the assumption that successive images have a similar segmentation. The boundary term is defined over the gray-scale intensity of the image. Experiments were conducted on nanoscale image sequences from the Serial Block Face Scanning Electron Microscope (SBF-SEM). The results show that our method can successfully track and segment densely packed cells in EM image stacks.
{"title":"Cell tracking and segmentation in electron microscopy images using graph cuts","authors":"Huei-Fang Yang, Y. Choe","doi":"10.1109/ISBI.2009.5193045","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193045","url":null,"abstract":"Understanding neural connectivity and structures in the brain requires detailed 3D anatomical models, and such an understanding is essential to the study of the nervous system. However, the reconstruction of 3D models from a large set of dense nanoscale medical images is very challenging, due to the imperfections in staining and noise in the imaging process. Manual segmentation in 2D followed by tracking the 2D contours through cross-sections to build 3D structures can be a solution, but it is impractical. In this paper, we propose an automated tracking and segmentation framework to extract 2D contours and to trace them through the z direction. The segmentation is posed as an energy minimization problem and solved via graph cuts. The energy function to be minimized contains a regional term and a boundary term. The regional term is defined over the flux of the gradient vector fields and the distance function. Our main idea is that the distance function should carry the information of the segmentation from the previous image based on the assumption that successive images have a similar segmentation. The boundary term is defined over the gray-scale intensity of the image. Experiments were conducted on nanoscale image sequences from the Serial Block Face Scanning Electron Microscope (SBF-SEM). The results show that our method can successfully track and segment densely packed cells in EM image stacks.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809600","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193218
Wei Feng, S. Reeves, T. Denney, S. Lloyd, L. Dell’Italia, H. Gupta
A consistent image registration formulation with B-spline deformation model is proposed. This formulation avoids the computation of the inverse of a deformation in the iterative optimization and allows the analytical computation of both gradient and Hessian of the cost function. Because of the formulation, the algorithm is computationally efficient and could potentially produce more accurate registration results. Experiments show that the proposed algorithm produces promising results while keeping the estimated deformations consistent.
{"title":"A new consistent image registration formulation with a B-spline deformation model","authors":"Wei Feng, S. Reeves, T. Denney, S. Lloyd, L. Dell’Italia, H. Gupta","doi":"10.1109/ISBI.2009.5193218","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193218","url":null,"abstract":"A consistent image registration formulation with B-spline deformation model is proposed. This formulation avoids the computation of the inverse of a deformation in the iterative optimization and allows the analytical computation of both gradient and Hessian of the cost function. Because of the formulation, the algorithm is computationally efficient and could potentially produce more accurate registration results. Experiments show that the proposed algorithm produces promising results while keeping the estimated deformations consistent.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927745","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193055
Barbara André, Tom Kamiel Magda Vercauteren, A. Perchant, A. Buchner, M. Wallace, N. Ayache
This paper investigates the use of modern content based image retrieval methods to classify endomicroscopic images into two categories: neoplastic (pathological) and benign. We describe first the method that maps an image into a visual feature signature which is a numerical vector invariant with respect to some particular classes of geometric and intensity transformations. Then we explain how these signatures are used to retrieve from a database the k closest images to a new image. The classification is finally achieved through a procedure of votes weighted by a proximity criterion (weighted k-nearest neighbors). Compared with several previously published alternatives whose maximal accuracy rate is almost 67% on the database, our approach yields an accuracy of 80% and offers promising perspectives.
{"title":"Endomicroscopic image retrieval and classification using invariant visual features","authors":"Barbara André, Tom Kamiel Magda Vercauteren, A. Perchant, A. Buchner, M. Wallace, N. Ayache","doi":"10.1109/ISBI.2009.5193055","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193055","url":null,"abstract":"This paper investigates the use of modern content based image retrieval methods to classify endomicroscopic images into two categories: neoplastic (pathological) and benign. We describe first the method that maps an image into a visual feature signature which is a numerical vector invariant with respect to some particular classes of geometric and intensity transformations. Then we explain how these signatures are used to retrieve from a database the k closest images to a new image. The classification is finally achieved through a procedure of votes weighted by a proximity criterion (weighted k-nearest neighbors). Compared with several previously published alternatives whose maximal accuracy rate is almost 67% on the database, our approach yields an accuracy of 80% and offers promising perspectives.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806157","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}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193027
Z. Qian, Idean Marvasty, Hunt Anderson, S. Rinehart, S. Voros
CT-based coronary artery calcium (CAC) scanning has been introduced as a non-invasive, low-radiation imaging technique for the assessment of the overall coronary arterial atherosclerotic burden. A three dimensional CAC volume contains significant clinically relevant information, which is unused by conventional whole-heart CAC quantification methods. In this paper, we have developed a more detailed distance-weighted lesion-specific CAC quantification framework that predicts cardiac events better than the conventional whole-heart CAC measures. This framework consists of (1) a novel lesion-specific CAC quantification tool that measures each calcific lesion's attenuation, morphologic and geometric statistics; (2) a distance-weighted event risk model to estimate the risk probability caused by each lesion, and (3) a Naive Bayesian technique for risk integration. We have tested our lesion-specific event predictor on 30 CAC positive scans (10 with events and 20 without events), and compared it with conventional whole-heart CAC scores. Experiment results showed our novel approach significantly improves the prediction accuracy, including AUC of ROC analysis was improved from 66 ∼ 68% to 75%, and sensitivities was improved by 20 ∼ 30% at the cutpoints of 80% specificity.
{"title":"Lesion-specific coronary artery calcium quantification better predicts cardiac events","authors":"Z. Qian, Idean Marvasty, Hunt Anderson, S. Rinehart, S. Voros","doi":"10.1109/ISBI.2009.5193027","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193027","url":null,"abstract":"CT-based coronary artery calcium (CAC) scanning has been introduced as a non-invasive, low-radiation imaging technique for the assessment of the overall coronary arterial atherosclerotic burden. A three dimensional CAC volume contains significant clinically relevant information, which is unused by conventional whole-heart CAC quantification methods. In this paper, we have developed a more detailed distance-weighted lesion-specific CAC quantification framework that predicts cardiac events better than the conventional whole-heart CAC measures. This framework consists of (1) a novel lesion-specific CAC quantification tool that measures each calcific lesion's attenuation, morphologic and geometric statistics; (2) a distance-weighted event risk model to estimate the risk probability caused by each lesion, and (3) a Naive Bayesian technique for risk integration. We have tested our lesion-specific event predictor on 30 CAC positive scans (10 with events and 20 without events), and compared it with conventional whole-heart CAC scores. Experiment results showed our novel approach significantly improves the prediction accuracy, including AUC of ROC analysis was improved from 66 ∼ 68% to 75%, and sensitivities was improved by 20 ∼ 30% at the cutpoints of 80% specificity.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083729","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}
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