Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490113
J. Frikel
We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.
{"title":"A new framework for sparse regularization in limited angle x-ray tomography","authors":"J. Frikel","doi":"10.1109/ISBI.2010.5490113","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490113","url":null,"abstract":"We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"44 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":"121719860","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490416
P. D. Tafti, R. Delgado-Gonzalo, A. Stalder, M. Unser
We introduce stochastic models for flow fields with parameters that dictate the scale-dependent (self-similar) character of the field and control the balance between its rotational vs compressive behaviour. The development of our models is motivated by the availability of imaging modalities that measure flow vector fields (flow-sensitive MRI and Doppler ultrasound). To study such data, we formulate estimators of the model parameters, and use them to quantify the Hurst exponent and directional properties of synthetic and real-world flow fields (measured by means of phase-contrast MRI) in 3D.
{"title":"Fractal modelling and analysis of flow-field images","authors":"P. D. Tafti, R. Delgado-Gonzalo, A. Stalder, M. Unser","doi":"10.1109/ISBI.2010.5490416","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490416","url":null,"abstract":"We introduce stochastic models for flow fields with parameters that dictate the scale-dependent (self-similar) character of the field and control the balance between its rotational vs compressive behaviour. The development of our models is motivated by the availability of imaging modalities that measure flow vector fields (flow-sensitive MRI and Doppler ultrasound). To study such data, we formulate estimators of the model parameters, and use them to quantify the Hurst exponent and directional properties of synthetic and real-world flow fields (measured by means of phase-contrast MRI) in 3D.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"39 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":"121274657","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490365
M. Guerquin-Kern, F. I. Karahanoğlu, D. Ville, K. Pruessmann, M. Unser
We present an analytical form of ground-truth k-space data for the 2-D Shepp-Logan brain phantom in the presence of multiple and non-homogeneous receiving coils. The analytical form allows us to conduct realistic simulations and validations of reconstruction algorithms for parallel MRI. The key contribution of our work is to use a polynomial representation of the coil's sensitivity. We show that this method is particularly accurate and fast with respect to the conventional methods. The implementation is made available to the community.
{"title":"Analytical form of Shepp-Logan phantom for parallel MRI","authors":"M. Guerquin-Kern, F. I. Karahanoğlu, D. Ville, K. Pruessmann, M. Unser","doi":"10.1109/ISBI.2010.5490365","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490365","url":null,"abstract":"We present an analytical form of ground-truth k-space data for the 2-D Shepp-Logan brain phantom in the presence of multiple and non-homogeneous receiving coils. The analytical form allows us to conduct realistic simulations and validations of reconstruction algorithms for parallel MRI. The key contribution of our work is to use a polynomial representation of the coil's sensitivity. We show that this method is particularly accurate and fast with respect to the conventional methods. The implementation is made available to the community.","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":"124173537","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490215
S. Chun, C. Schretter, J. Fessler
Recent advances in medical imaging technologies have made 4D image sequences available in clinical routine. As a consequence, image registration techniques are evolving from alignment of pairs of static volumetric images to spatio-temporal registration of dynamic (4D) images. Since the elastic image registration problem is ill-posed, additional prior information or constraints are usually required to regularize the problem. This work proposes to enforce local invertibility (diffeomorphism) of 4D deformations. A novel sufficient condition for local invertibility over continuous space and time is proposed and a practical regularization prior is designed from the theory. The method has been applied to an image registration (motion tracking) of a dynamic 4D CT image sequence. Results show that using proposed regularizer leads to deformations that are more plausible for respiratory motion than the standard approach without additional temporal regularization.
{"title":"Sufficient condition for local invertibility of spatio-temporal 4D B-spline deformations","authors":"S. Chun, C. Schretter, J. Fessler","doi":"10.1109/ISBI.2010.5490215","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490215","url":null,"abstract":"Recent advances in medical imaging technologies have made 4D image sequences available in clinical routine. As a consequence, image registration techniques are evolving from alignment of pairs of static volumetric images to spatio-temporal registration of dynamic (4D) images. Since the elastic image registration problem is ill-posed, additional prior information or constraints are usually required to regularize the problem. This work proposes to enforce local invertibility (diffeomorphism) of 4D deformations. A novel sufficient condition for local invertibility over continuous space and time is proposed and a practical regularization prior is designed from the theory. The method has been applied to an image registration (motion tracking) of a dynamic 4D CT image sequence. Results show that using proposed regularizer leads to deformations that are more plausible for respiratory motion than the standard approach without additional temporal regularization.","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":"122368246","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490103
M. Langer, F. Peyrin
In zoom-in tomography, the aim is to image a region of interest lying partially or fully within the imaged object, using a high resolution tomographic scan covering only the ROI, and a low resolution scan covering the whole object. We analyze the problem from a multiresolution point of view and propose an algorithm for combining the two data sets using the discrete wavelet transform and the Haar wavelet. We compare the proposed algorithm to a previously reported method that involves padding of the high resolution data with a supersampled version of the low resolution data, to zero padding and edge extension, using synthetic data sets. We show that the proposed algorithm is insensitive to offsets between the two data sets, but that it is slightly more sensitive to noise.
{"title":"A wavelet algorithm for zoom-in tomography","authors":"M. Langer, F. Peyrin","doi":"10.1109/ISBI.2010.5490103","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490103","url":null,"abstract":"In zoom-in tomography, the aim is to image a region of interest lying partially or fully within the imaged object, using a high resolution tomographic scan covering only the ROI, and a low resolution scan covering the whole object. We analyze the problem from a multiresolution point of view and propose an algorithm for combining the two data sets using the discrete wavelet transform and the Haar wavelet. We compare the proposed algorithm to a previously reported method that involves padding of the high resolution data with a supersampled version of the low resolution data, to zero padding and edge extension, using synthetic data sets. We show that the proposed algorithm is insensitive to offsets between the two data sets, but that it is slightly more sensitive to noise.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"256 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":"127929954","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490429
C. I. Sánchez, M. Niemeijer, M. Suttorp-Schulten, M. Abràmoff, B. Ginneken
Contextual information is of paramount importance in medical image understanding to detect and differentiate pathologies, especially when interpreting difficult cases. Current computer-aided detection (CAD) systems typically employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this work, we improve the detection of hard exudates in retinal images incorporating contextual information in the CAD system. The context is described by means of high-level contextual-based features based on the spatial relation with surrounding anatomical landmarks and similar lesions. Results show that a contextual CAD system for hard exudate detection is superior to an approach that uses only local information, with a significant increase of the figure of merit of the Free Receiver Operating Characteristic (FROC) curve from 0.840 to 0.945.
{"title":"Improving hard exudate detection in retinal images through a combination of local and contextual information","authors":"C. I. Sánchez, M. Niemeijer, M. Suttorp-Schulten, M. Abràmoff, B. Ginneken","doi":"10.1109/ISBI.2010.5490429","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490429","url":null,"abstract":"Contextual information is of paramount importance in medical image understanding to detect and differentiate pathologies, especially when interpreting difficult cases. Current computer-aided detection (CAD) systems typically employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this work, we improve the detection of hard exudates in retinal images incorporating contextual information in the CAD system. The context is described by means of high-level contextual-based features based on the spatial relation with surrounding anatomical landmarks and similar lesions. Results show that a contextual CAD system for hard exudate detection is superior to an approach that uses only local information, with a significant increase of the figure of merit of the Free Receiver Operating Characteristic (FROC) curve from 0.840 to 0.945.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 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":"129646837","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490138
N. Honnorat, Régis Vaillant, N. Paragios
Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.
{"title":"Robust guidewire segmentation through boosting, clustering and linear programming","authors":"N. Honnorat, Régis Vaillant, N. Paragios","doi":"10.1109/ISBI.2010.5490138","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490138","url":null,"abstract":"Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 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":"129766996","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490237
E. Shaked, O. Michailovich
The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by measurement noise. In the field of optics and nuclear imaging, the noise is commonly assumed to obey a Poisson distribution. In this note, a novel method for de-noising and/or de-blurring of digital images corrupted by Poisson noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees convergence to the global maximizer of an associated maximuma-posteriori criterion.
{"title":"Deconvolution of poissonian images via iterative shrinkage","authors":"E. Shaked, O. Michailovich","doi":"10.1109/ISBI.2010.5490237","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490237","url":null,"abstract":"The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by measurement noise. In the field of optics and nuclear imaging, the noise is commonly assumed to obey a Poisson distribution. In this note, a novel method for de-noising and/or de-blurring of digital images corrupted by Poisson noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees convergence to the global maximizer of an associated maximuma-posteriori criterion.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 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":"130313534","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490091
Y. Artan, M. Haider, Deanne L. Langei, I. Yetik
Prostate cancer is one of the leading causes of cancer related death for men in the United States. Recently, multispectral magnetic resonance imaging (MRI) has emerged as a promising noninvasive method for the localization of prostate cancer alternative to transrectal ultrasound (TRUS). This paper develops a semi-supervised method for prostate cancer localization using multispectral MRI. Patient-specific contrast can be utilized in this method for improved performance. We also propose to use an anisotropic filtering scheme to suppress the noise in the images. Using multispectral MR images, we demonstrate the effectiveness of this algorithm by testing it on real data sets and compare it to the results of a fully-automated method as well as to the earlier results. Both visual and quantitative comparisons are provided, illlustrating the success of the proposed method.
{"title":"Semi-supervised prostate cancer segmentation with multispectral MRI","authors":"Y. Artan, M. Haider, Deanne L. Langei, I. Yetik","doi":"10.1109/ISBI.2010.5490091","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490091","url":null,"abstract":"Prostate cancer is one of the leading causes of cancer related death for men in the United States. Recently, multispectral magnetic resonance imaging (MRI) has emerged as a promising noninvasive method for the localization of prostate cancer alternative to transrectal ultrasound (TRUS). This paper develops a semi-supervised method for prostate cancer localization using multispectral MRI. Patient-specific contrast can be utilized in this method for improved performance. We also propose to use an anisotropic filtering scheme to suppress the noise in the images. Using multispectral MR images, we demonstrate the effectiveness of this algorithm by testing it on real data sets and compare it to the results of a fully-automated method as well as to the earlier results. Both visual and quantitative comparisons are provided, illlustrating the success of the proposed method.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"9 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":"130494311","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490424
Marie-Paule Garcia, C. Toumoulin, P. Haigron, J. Velut, M. Garreau, D. Boulmier
In this paper, we deal with the problem of tracking the coronary venous tree from Multi-Slice Computed Tomography (MSCT) angiography. Contrast inhomogeneities are a major issue. The proposed tracking procedure is based on minimum-cost path computation and makes use of ‘Fast-Marching’ technique. The algorithm aims at propagating a front inside a vascular structure and extracting a centered path. To achieve this goal, a specific cost function which combines the vessel local orientation to a vesselness measure is designed. Experiments on synthetic data and real data have been performed. Coronary veins with contrast difficulties are extracted with a low computing time.
{"title":"Coronary vein tracking from MSCT using a minimum cost path approach","authors":"Marie-Paule Garcia, C. Toumoulin, P. Haigron, J. Velut, M. Garreau, D. Boulmier","doi":"10.1109/ISBI.2010.5490424","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490424","url":null,"abstract":"In this paper, we deal with the problem of tracking the coronary venous tree from Multi-Slice Computed Tomography (MSCT) angiography. Contrast inhomogeneities are a major issue. The proposed tracking procedure is based on minimum-cost path computation and makes use of ‘Fast-Marching’ technique. The algorithm aims at propagating a front inside a vascular structure and extracting a centered path. To achieve this goal, a specific cost function which combines the vessel local orientation to a vesselness measure is designed. Experiments on synthetic data and real data have been performed. Coronary veins with contrast difficulties are extracted with a low computing time.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"54 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":"130642417","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：