Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541323
J. C. Ye, Su Yeon Lee, Y. Bresler
Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality. However, the inverse problem of reconstructing optical parameters from scattered light measurements is highly nonlinear due to the nonlinear coupling between the optical coefficients and the photon flux in the diffusion equation. Even though nonlinear iterative methods have been commonly used, such iterative processes are computationally expensive especially for the three dimensional imaging scenario with massive number of detector elements. The main contribution of this paper is a novel non-iterative and exact inversion algorithm when the optical inhomogeneities are sparsely distributed. We show that the problem can be converted into simultaneous sparse representation problem with multiple measurement vectors from compressed sensing framework. The exact reconstruction formula is obtained using simultaneous orthogonal matching pursuit (S-OMP) and a simple two step approach without ever calculating the diffusion equation. Simulation results also confirm our theory.
{"title":"Exact reconstruction formula for diffuse optical tomography using simultaneous sparse representation","authors":"J. C. Ye, Su Yeon Lee, Y. Bresler","doi":"10.1109/ISBI.2008.4541323","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541323","url":null,"abstract":"Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality. However, the inverse problem of reconstructing optical parameters from scattered light measurements is highly nonlinear due to the nonlinear coupling between the optical coefficients and the photon flux in the diffusion equation. Even though nonlinear iterative methods have been commonly used, such iterative processes are computationally expensive especially for the three dimensional imaging scenario with massive number of detector elements. The main contribution of this paper is a novel non-iterative and exact inversion algorithm when the optical inhomogeneities are sparsely distributed. We show that the problem can be converted into simultaneous sparse representation problem with multiple measurement vectors from compressed sensing framework. The exact reconstruction formula is obtained using simultaneous orthogonal matching pursuit (S-OMP) and a simple two step approach without ever calculating the diffusion equation. Simulation results also confirm our theory.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804408","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541080
W. Nailon, A. Redpath, D. McLaren
At present no single texture analysis approach can provide automatic classification to the accuracy required for radiotherapy applications. The method presented was developed to classify areas within the gross tumor volume (GTV), and other clinically relevant regions, on computerized tomography (CT) images. For eight bladder cancer patients, CT information was acquired at the radiotherapy planning stage and thereafter at regular intervals during treatment. Textural features (N=27) were calculated on regions extracted within the bladder, rectum and a region identified as clinically relevant. The sequential forward search (SFS) method was used to reduce the feature set (N=3). The results demonstrate the significant sensitivity of the reduced feature set for classification of any orthogonal CT image and the potential of the approach for radiotherapy applications.
{"title":"Texture analysis of 3D bladder cancer CT images for improving radiotherapy planning","authors":"W. Nailon, A. Redpath, D. McLaren","doi":"10.1109/ISBI.2008.4541080","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541080","url":null,"abstract":"At present no single texture analysis approach can provide automatic classification to the accuracy required for radiotherapy applications. The method presented was developed to classify areas within the gross tumor volume (GTV), and other clinically relevant regions, on computerized tomography (CT) images. For eight bladder cancer patients, CT information was acquired at the radiotherapy planning stage and thereafter at regular intervals during treatment. Textural features (N=27) were calculated on regions extracted within the bladder, rectum and a region identified as clinically relevant. The sequential forward search (SFS) method was used to reduce the feature set (N=3). The results demonstrate the significant sensitivity of the reduced feature set for classification of any orthogonal CT image and the potential of the approach for radiotherapy applications.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135158","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541293
P. D. Tafti, D. Ville, M. Unser
Growth and form in biology are often associated with some level of fractality. Fractal characteristics have also been noted in a number of imaging modalities. These observations make fractal modelling relevant in the context of bio-imaging. In this paper, we introduce a simple and yet rigorous innovation model for multi-dimensional fractional Brownian motion (fBm) and provide the computational tools for the analysis of such processes in a multi-resolution framework. The key point is that these processes can be whitened by application of the appropriate fractional Lapla-cian operator which has a corresponding polyharmonic wavelet. We examine the case of MRI and mammography images through comparison with theoretical results, which underline the suitability of fractal models in the study of bio-textures.
{"title":"Innovation modelling and wavelet analysis of fractal processes in bio-imaging","authors":"P. D. Tafti, D. Ville, M. Unser","doi":"10.1109/ISBI.2008.4541293","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541293","url":null,"abstract":"Growth and form in biology are often associated with some level of fractality. Fractal characteristics have also been noted in a number of imaging modalities. These observations make fractal modelling relevant in the context of bio-imaging. In this paper, we introduce a simple and yet rigorous innovation model for multi-dimensional fractional Brownian motion (fBm) and provide the computational tools for the analysis of such processes in a multi-resolution framework. The key point is that these processes can be whitened by application of the appropriate fractional Lapla-cian operator which has a corresponding polyharmonic wavelet. We examine the case of MRI and mammography images through comparison with theoretical results, which underline the suitability of fractal models in the study of bio-textures.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115163576","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4540924
U. Kurkure, Olga C. Avila-Montes, I. Kakadiaris
Aortic calcification has been shown to be related to cardiovascular disease. In this paper, we present a novel method for localization and segmentation of thoracic aorta in non- contrast CT images using dynamic programming concepts to detect and quantify aortic calcium. The localization and segmentation of the aorta are formulated as optimal path detection problems, which are solved using dynamic programming principles. We apply these methods on Hough space for aorta localization and a transformed polar coordinate space for aorta segmentation. We evaluate the proposed approach by comparing it with the manual annotations in terms of aorta location, boundary distance, and volume overlap.
{"title":"Automated segmentation of thoracic aorta in non-contrast CT images","authors":"U. Kurkure, Olga C. Avila-Montes, I. Kakadiaris","doi":"10.1109/ISBI.2008.4540924","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540924","url":null,"abstract":"Aortic calcification has been shown to be related to cardiovascular disease. In this paper, we present a novel method for localization and segmentation of thoracic aorta in non- contrast CT images using dynamic programming concepts to detect and quantify aortic calcium. The localization and segmentation of the aorta are formulated as optimal path detection problems, which are solved using dynamic programming principles. We apply these methods on Hough space for aorta localization and a transformed polar coordinate space for aorta segmentation. We evaluate the proposed approach by comparing it with the manual annotations in terms of aorta location, boundary distance, and volume overlap.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116926013","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541154
Yu Bai, D. Alexander
In diffusion tensor MRI, a number of diffusion-weighted images with different diffusion-weighting gradient directions are acquired during scanning. The tensor calculation assumes that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. The traditional method uses a non-diffusion- weighted image as the reference for registration, but the differences between diffusion-weighted images and the non- diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like the mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. We demonstrate quantitative improvements using the new approaches on a full data with slight, but typical, movement during acquisition.
{"title":"Model-based registration to correct for motion between acquisitions in diffusion MR imaging","authors":"Yu Bai, D. Alexander","doi":"10.1109/ISBI.2008.4541154","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541154","url":null,"abstract":"In diffusion tensor MRI, a number of diffusion-weighted images with different diffusion-weighting gradient directions are acquired during scanning. The tensor calculation assumes that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. The traditional method uses a non-diffusion- weighted image as the reference for registration, but the differences between diffusion-weighted images and the non- diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like the mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. We demonstrate quantitative improvements using the new approaches on a full data with slight, but typical, movement during acquisition.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490768","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541137
E. Davoodi-bojd, H. Soltanian-Zadeh
In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.
{"title":"Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors","authors":"E. Davoodi-bojd, H. Soltanian-Zadeh","doi":"10.1109/ISBI.2008.4541137","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541137","url":null,"abstract":"In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732724","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541295
Leonid Teverovskiy, J. Becker, O. Lopez, Yanxi Liu
We propose a quantified asymmetry based method for age estimation. Our method uses machine learning to discover automatically the most discriminative asymmetry feature set from different brain regions and image scales. Applying this regression model on a Tl MR brain image set of 246 healthy individuals (121 females; 125 males, 66 plusmn 7.5 years old), we achieve a mean absolute error of 5.4 years and a mean signed error of -0.2 years for age estimation on unseen MR images using the stringent leave-15%-out cross validation. Our results show significant changes in asymmetry with aging in the following regions: the posterior horns of the lateral ventricles, the amygdala, the ventral putamen with a nearby region of the anterior inferior caudate nucleus, the basal fore- brain, hyppocampus and parahyppocampal regions. We confirm the validity of the age estimation model using permutation test on 30 replicas of the original dataset with randomly permuted ages (with p-value < 0.001). Furthermore, we apply this model to a separate set of MR images containing normal, Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects. Our results reflect the relative severity of brain pathology between the three subject groups: mean signed age estimation error is 0.6 years for normal controls, 2.2 years for MCI patients, and 4.7 years for AD patients.
{"title":"Quantified brain asymmetry for age estimation of normal and AD/MCI subjects","authors":"Leonid Teverovskiy, J. Becker, O. Lopez, Yanxi Liu","doi":"10.1109/ISBI.2008.4541295","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541295","url":null,"abstract":"We propose a quantified asymmetry based method for age estimation. Our method uses machine learning to discover automatically the most discriminative asymmetry feature set from different brain regions and image scales. Applying this regression model on a Tl MR brain image set of 246 healthy individuals (121 females; 125 males, 66 plusmn 7.5 years old), we achieve a mean absolute error of 5.4 years and a mean signed error of -0.2 years for age estimation on unseen MR images using the stringent leave-15%-out cross validation. Our results show significant changes in asymmetry with aging in the following regions: the posterior horns of the lateral ventricles, the amygdala, the ventral putamen with a nearby region of the anterior inferior caudate nucleus, the basal fore- brain, hyppocampus and parahyppocampal regions. We confirm the validity of the age estimation model using permutation test on 30 replicas of the original dataset with randomly permuted ages (with p-value < 0.001). Furthermore, we apply this model to a separate set of MR images containing normal, Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects. Our results reflect the relative severity of brain pathology between the three subject groups: mean signed age estimation error is 0.6 years for normal controls, 2.2 years for MCI patients, and 4.7 years for AD patients.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493434","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541007
J. Angulo, B. Schaack
In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloi'din) on nanodrops cell-on-chip format.
{"title":"Morphological-based adaptive segmentation and quantification of cell assays in high content screening","authors":"J. Angulo, B. Schaack","doi":"10.1109/ISBI.2008.4541007","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541007","url":null,"abstract":"In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloi'din) on nanodrops cell-on-chip format.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625214","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4540942
J. Kaftan, A. Bakai, M. Das, T. Aach
Pulmonary vascular tree segmentation is the fundamental basis for different applications, such as the detection and visualization of pulmonary emboli (PE). Such an application requires an accurate and reliable segmentation of pulmonary vessels with varying diameters. We present a novel fuzzy approach to pulmonary vessel segmentation in contrast enhanced computed tomography (CT) data that considers a radius estimate of the current vessel to adapt the segmentation parameters. Hence, our method allows to capture even vessels with small diameters while suppressing leakage into surrounding structures in close proximity of vessels with large diameters. The method has been evaluated on different chest CT scans of patients referred for PE and demonstrates promising results. For quantitative validation, randomly selected sub-volumes that have been semi-automatically segmented by a medical expert have been used as reference to compare the locally adaptive method against the same method with global parameters.
{"title":"Locally adaptive fuzzy pulmonary vessel segmentation in contrast enhanced CT data","authors":"J. Kaftan, A. Bakai, M. Das, T. Aach","doi":"10.1109/ISBI.2008.4540942","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540942","url":null,"abstract":"Pulmonary vascular tree segmentation is the fundamental basis for different applications, such as the detection and visualization of pulmonary emboli (PE). Such an application requires an accurate and reliable segmentation of pulmonary vessels with varying diameters. We present a novel fuzzy approach to pulmonary vessel segmentation in contrast enhanced computed tomography (CT) data that considers a radius estimate of the current vessel to adapt the segmentation parameters. Hence, our method allows to capture even vessels with small diameters while suppressing leakage into surrounding structures in close proximity of vessels with large diameters. The method has been evaluated on different chest CT scans of patients referred for PE and demonstrates promising results. For quantitative validation, randomly selected sub-volumes that have been semi-automatically segmented by a medical expert have been used as reference to compare the locally adaptive method against the same method with global parameters.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124381362","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541237
Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu
3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.
{"title":"AutoMPR: Automatic detection of standard planes in 3D echocardiography","authors":"Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu","doi":"10.1109/ISBI.2008.4541237","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541237","url":null,"abstract":"3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465973","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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