Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867955
Brian L. Burns, N. Wilson, M. Thomas
4D Magnetic Resonance Spectroscopic Imaging data provides valuable biochemical information in vivo, however, its acquisition time is too long to be used clinically. In this paper, 4D phantom MRSI data are retrospectively under-sampled 4X, 6X, and 8X then reconstructed with Compressed Sensing and Group Sparsity. A derivation for the Group Sparse problem solution within the Split-Bregman framework is provided which allows for arbitrary, over-lapping groups of transform coefficients. Results show that Group Sparse reconstruction with over-lapping groups is more accurate at each under-sampling rate than Compressed Sensing reconstruction with superior peak line-shape and amplitude reproduction. The acceleration factors used in these experiments could potentially reduce scan times from 40 minutes to 5 minutes.
{"title":"Split-Bregman-based group-sparse reconstruction of multidimensional spectroscopic imaging data","authors":"Brian L. Burns, N. Wilson, M. Thomas","doi":"10.1109/ISBI.2014.6867955","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867955","url":null,"abstract":"4D Magnetic Resonance Spectroscopic Imaging data provides valuable biochemical information in vivo, however, its acquisition time is too long to be used clinically. In this paper, 4D phantom MRSI data are retrospectively under-sampled 4X, 6X, and 8X then reconstructed with Compressed Sensing and Group Sparsity. A derivation for the Group Sparse problem solution within the Split-Bregman framework is provided which allows for arbitrary, over-lapping groups of transform coefficients. Results show that Group Sparse reconstruction with over-lapping groups is more accurate at each under-sampling rate than Compressed Sensing reconstruction with superior peak line-shape and amplitude reproduction. The acceleration factors used in these experiments could potentially reduce scan times from 40 minutes to 5 minutes.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"47 12 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":"132392066","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867992
Da Chen, L. Cohen
In this paper, we propose a completely automatic method to extract the vessel tree structure including its centerlines and radius using geodesic paths technology. Our main goal is to find a set of key points located in the vessel centerlines, link each pair of key points by minimal paths for finding the vessels between them and stop this process automatically. This work adapts the growing minimal paths method to find the set of key points. The main drawback of growing minimal paths is when to stop the processing. To solve this problem we propose an automatic stopping criteria. Additionally, we use a cake wavelet to compute the vessel measurement consisting of both radius and orientation to develop the classical growing minimal paths model which cannot guarantee the extracted tree corresponds to the centerlines of the vessel tree.
{"title":"Automatic vessel tree structure extraction by growing minimal paths and a mask","authors":"Da Chen, L. Cohen","doi":"10.1109/ISBI.2014.6867992","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867992","url":null,"abstract":"In this paper, we propose a completely automatic method to extract the vessel tree structure including its centerlines and radius using geodesic paths technology. Our main goal is to find a set of key points located in the vessel centerlines, link each pair of key points by minimal paths for finding the vessels between them and stop this process automatically. This work adapts the growing minimal paths method to find the set of key points. The main drawback of growing minimal paths is when to stop the processing. To solve this problem we propose an automatic stopping criteria. Additionally, we use a cake wavelet to compute the vessel measurement consisting of both radius and orientation to develop the classical growing minimal paths model which cannot guarantee the extracted tree corresponds to the centerlines of the vessel tree.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 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":"129431263","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868059
John M. Batikian, M. Liebling
Non-local means (NLM) noise reduction is effective yet computationally intensive, since each output pixel is a weighted average of all input pixels. Most implementations therefore restrict the scope of the average to a smaller neighborhood around each pixel, which limits the method's full potential. Here we propose to apply NLM to reduce the noise in fluorescence microscopy image sequences of the beating heart, whose quasi-repeatable pattern produces multiple realizations of similar image patches in different heart beats. We propose to restrict the averaging from the entire sequence to a subset of globally similar frames across multiple heart beats. Using a high-SNR brightfield microscopy image sequence of a beating embryonic zebrafish heart that we artificially corrupt with noise, we illustrate the benefits of selecting non-adjacent frames rather than the immediate temporal neighborhood in the vicinity of the pixel being denoised. The image quality of our NLM approach is also better than that obtained by directly computing the sample median of matching frames over multiple heartbeats, a commonly used method. Finally, we demonstrate the applicability of our proposed scheme to low-intensity fluorescence images of the embryonic zebrafish heart.
{"title":"Multicycle non-local means denoising of cardiac image sequences","authors":"John M. Batikian, M. Liebling","doi":"10.1109/ISBI.2014.6868059","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868059","url":null,"abstract":"Non-local means (NLM) noise reduction is effective yet computationally intensive, since each output pixel is a weighted average of all input pixels. Most implementations therefore restrict the scope of the average to a smaller neighborhood around each pixel, which limits the method's full potential. Here we propose to apply NLM to reduce the noise in fluorescence microscopy image sequences of the beating heart, whose quasi-repeatable pattern produces multiple realizations of similar image patches in different heart beats. We propose to restrict the averaging from the entire sequence to a subset of globally similar frames across multiple heart beats. Using a high-SNR brightfield microscopy image sequence of a beating embryonic zebrafish heart that we artificially corrupt with noise, we illustrate the benefits of selecting non-adjacent frames rather than the immediate temporal neighborhood in the vicinity of the pixel being denoised. The image quality of our NLM approach is also better than that obtained by directly computing the sample median of matching frames over multiple heartbeats, a commonly used method. Finally, we demonstrate the applicability of our proposed scheme to low-intensity fluorescence images of the embryonic zebrafish heart.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 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":"132302742","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867924
Junjie Bai, Mohammad Saleh Miri, Yinxiao Liu, P. Saha, M. Garvin, Xiaodong Wu
A novel graph-based optimal segmentation method which can simultaneously segment multiple star-shaped surfaces is presented in this paper. Minimum and maximum surface distance constraints can be enforced between different surfaces. In addition, the segmented surfaces are ensured to be smooth by incorporating surface smoothness constraints which limit the variation between adjacent surface voxels. A consistent digital ray system is utilized to make sure the segmentation result is star-shaped and consistent, without interpolating image as required by other methods. To the best of our knowledge, the concept of consistent digital rays is for the first time introduced into the field of medical imaging. The problem is formulated as an MRF optimization problem which can be efficiently and exactly solved by computing a single min s-t cut in an appropriately constructed graph. The method is applied to the segmentation of the optic disc and cup on 70 registered fundus and SD-OCT images from glaucoma patients. The result shows improved accuracy by applying the proposed method (versus using a classification-based approach).
{"title":"Graph-based optimal multi-surface segmentation with a star-shaped prior: Application to the segmentation of the optic disc and cup","authors":"Junjie Bai, Mohammad Saleh Miri, Yinxiao Liu, P. Saha, M. Garvin, Xiaodong Wu","doi":"10.1109/ISBI.2014.6867924","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867924","url":null,"abstract":"A novel graph-based optimal segmentation method which can simultaneously segment multiple star-shaped surfaces is presented in this paper. Minimum and maximum surface distance constraints can be enforced between different surfaces. In addition, the segmented surfaces are ensured to be smooth by incorporating surface smoothness constraints which limit the variation between adjacent surface voxels. A consistent digital ray system is utilized to make sure the segmentation result is star-shaped and consistent, without interpolating image as required by other methods. To the best of our knowledge, the concept of consistent digital rays is for the first time introduced into the field of medical imaging. The problem is formulated as an MRF optimization problem which can be efficiently and exactly solved by computing a single min s-t cut in an appropriately constructed graph. The method is applied to the segmentation of the optic disc and cup on 70 registered fundus and SD-OCT images from glaucoma patients. The result shows improved accuracy by applying the proposed method (versus using a classification-based approach).","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"46 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":"133755853","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867928
Mariña López-Yunta, T. Lagache, J. Santi-Rocca, P. Bastin, J. Olivo-Marin
The analysis of the spatial distribution of molecules along one dimensional structures, such as filaments of the cell's cytoskeleton, gives essential information on intracellular transport mechanisms. The standard tool for analyzing molecules' organizationis the Ripley's K function, which permits to statistically test the hypothesis of molecules' random distribution against clustering or dispersion. However, the computation of the critical quantiles of Ripley's K function is currently based on Monte-Carlo simulations, which induces a high computational load and hinders its use. Here, we present an analytical expression of these quantiles for 1D filaments, leading to a fast and robust statistical test. Thereafter, we used our statistical test to analyze the spatial distribution of proteins involved in intraflagellar transport along the flagellum of the parasite Typanosoma brucei.
{"title":"A statistical analysis of spatial clustering along cell filaments using Ripley's K function","authors":"Mariña López-Yunta, T. Lagache, J. Santi-Rocca, P. Bastin, J. Olivo-Marin","doi":"10.1109/ISBI.2014.6867928","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867928","url":null,"abstract":"The analysis of the spatial distribution of molecules along one dimensional structures, such as filaments of the cell's cytoskeleton, gives essential information on intracellular transport mechanisms. The standard tool for analyzing molecules' organizationis the Ripley's K function, which permits to statistically test the hypothesis of molecules' random distribution against clustering or dispersion. However, the computation of the critical quantiles of Ripley's K function is currently based on Monte-Carlo simulations, which induces a high computational load and hinders its use. Here, we present an analytical expression of these quantiles for 1D filaments, leading to a fast and robust statistical test. Thereafter, we used our statistical test to analyze the spatial distribution of proteins involved in intraflagellar transport along the flagellum of the parasite Typanosoma brucei.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"72 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":"130141095","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868126
L. Rojas, G. Martinez, T. Scheper
In this contribution, a new algorithm to estimate the cell count from an intensity image of Baby Hamster Kidney (BHK) cells captured by an in-situ microscope is proposed. Given that the local intensity maxima inside a cell share similar location and intensity values, it is proposed to find all the intensity maxima inside each cell cluster present in the image, and then group those who share similar location and intensity values. The total number of cells present in an image is estimated as the sum of the number of groups found in each cluster. The experimental results show that the average cell count improved by 79%, and that the average image processing time improved by 42%.
{"title":"Cell counting based on local intensity maxima grouping for in-situ microscopy","authors":"L. Rojas, G. Martinez, T. Scheper","doi":"10.1109/ISBI.2014.6868126","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868126","url":null,"abstract":"In this contribution, a new algorithm to estimate the cell count from an intensity image of Baby Hamster Kidney (BHK) cells captured by an in-situ microscope is proposed. Given that the local intensity maxima inside a cell share similar location and intensity values, it is proposed to find all the intensity maxima inside each cell cluster present in the image, and then group those who share similar location and intensity values. The total number of cells present in an image is estimated as the sum of the number of groups found in each cluster. The experimental results show that the average cell count improved by 79%, and that the average image processing time improved by 42%.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"80 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":"116257943","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868129
Fan Zhang, Yang Song, Weidong (Tom) Cai, Yun Zhou, M. Fulham, S. Eberl, S. Shan, D. Feng
In this paper, we propose a novel semi-supervised classification method for four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, in low dose computed tomography (LDCT) scans. The proposed method focuses on classifier design by incorporating the knowledge extracted from both training and testing datasets, and contains two stages: (1) bipartite graph construction, which presents the direct similar relationship between labeled and unlabeled images, (2) ranking score calculation, which computes the possibility of unlabeled images for each of the given four types. Our proposed method is evaluated on a publicly available dataset and clearly demonstrates its promising classification performance.
{"title":"A ranking-based lung nodule image classification method using unlabeled image knowledge","authors":"Fan Zhang, Yang Song, Weidong (Tom) Cai, Yun Zhou, M. Fulham, S. Eberl, S. Shan, D. Feng","doi":"10.1109/ISBI.2014.6868129","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868129","url":null,"abstract":"In this paper, we propose a novel semi-supervised classification method for four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, in low dose computed tomography (LDCT) scans. The proposed method focuses on classifier design by incorporating the knowledge extracted from both training and testing datasets, and contains two stages: (1) bipartite graph construction, which presents the direct similar relationship between labeled and unlabeled images, (2) ranking score calculation, which computes the possibility of unlabeled images for each of the given four types. Our proposed method is evaluated on a publicly available dataset and clearly demonstrates its promising classification performance.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"8 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":"114448617","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867812
M. D. Craene, M. Alessandrini, P. Allain, S. Marchesseau, Irina Wächter-Stehle, J. Weese, E. Saloux, H. Morales, R. Cuingnet, H. Delingette, Maxime Sermesant, O. Bernard, J. D’hooge
This paper proposes a new simulation framework for generating realistic 3D ultrasound synthetic images that can serve for validating strain quantification algorithms. Our approach extends previous work and combines a real ultrasound sequence with synthetic biomechanical and ultrasound models. It provides images that fairly represent all typical ultrasound artifacts. Ground truth motion fields are unbiased to any tracking algorithm and model both healthy and pathological conditions.
{"title":"Generation of ultra-realistic synthetic echocardiographic sequences","authors":"M. D. Craene, M. Alessandrini, P. Allain, S. Marchesseau, Irina Wächter-Stehle, J. Weese, E. Saloux, H. Morales, R. Cuingnet, H. Delingette, Maxime Sermesant, O. Bernard, J. D’hooge","doi":"10.1109/ISBI.2014.6867812","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867812","url":null,"abstract":"This paper proposes a new simulation framework for generating realistic 3D ultrasound synthetic images that can serve for validating strain quantification algorithms. Our approach extends previous work and combines a real ultrasound sequence with synthetic biomechanical and ultrasound models. It provides images that fairly represent all typical ultrasound artifacts. Ground truth motion fields are unbiased to any tracking algorithm and model both healthy and pathological conditions.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"56 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":"114572592","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867951
Jan-Philip Bergeest, K. Rohr
Accurate and efficient segmentation of cell nuclei in 3D fluorescence microscopy images is important for the quantification of cellular processes. We propose a new 3D segmentation approach for cell nuclei which is based on level set deformable models and convex minimization. Our approach employs different convex energy functionals, uses an efficient numeric method for minimization, and integrates a scheme for cell splitting. Compared to previous level set approaches for 3D cell microscopy images, our approach determines global solutions. The performance of our approach has been evaluated using in vivo 3D fluorescence microscopy images. We have also performed a quantitative comparison with previous 3D segmentation approaches.
{"title":"Segmentation of cell nuclei in 3D microscopy images based on level set deformable models and convex minimization","authors":"Jan-Philip Bergeest, K. Rohr","doi":"10.1109/ISBI.2014.6867951","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867951","url":null,"abstract":"Accurate and efficient segmentation of cell nuclei in 3D fluorescence microscopy images is important for the quantification of cellular processes. We propose a new 3D segmentation approach for cell nuclei which is based on level set deformable models and convex minimization. Our approach employs different convex energy functionals, uses an efficient numeric method for minimization, and integrates a scheme for cell splitting. Compared to previous level set approaches for 3D cell microscopy images, our approach determines global solutions. The performance of our approach has been evaluated using in vivo 3D fluorescence microscopy images. We have also performed a quantitative comparison with previous 3D segmentation approaches.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"43 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":"132039828","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867944
Emmanuel Soubies, L. Blanc-Féraud, S. Schaub, G. Aubert
We propose a new model for the reconstruction of biological structures using Multiple-Angle Total Internal Reflection Fluorescence Microscopy (MA-TIRFM). This recent microscopy technique allows the visualization of sub-cellular structures around the plasma membrane which is of fundamental importance in the comprehension of exchanges mechanisms of the cell. We present a 3D reconstruction method based on a shape prior information on the observed structures and robust to shot noise and background fluorescence. A novelty with respect to the state of the art is to propose a method allowing the recovery of multiple objects aligned along the axial axis. The optimization problem can be formulated as a minimization problem where both the number of objects in the model and their parameters have to be estimated. This difficult combinatorial optimization problem is tackled by using a Marked Point Process approach which allows modelling interactions between the objects in order to regularize the inverse problem. Finally, performances of the proposed method are evaluated on synthetic data and real data.
{"title":"A 3D model with shape prior information for biological structures reconstruction using multiple-angle total internal reflection fluorescence microscopy","authors":"Emmanuel Soubies, L. Blanc-Féraud, S. Schaub, G. Aubert","doi":"10.1109/ISBI.2014.6867944","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867944","url":null,"abstract":"We propose a new model for the reconstruction of biological structures using Multiple-Angle Total Internal Reflection Fluorescence Microscopy (MA-TIRFM). This recent microscopy technique allows the visualization of sub-cellular structures around the plasma membrane which is of fundamental importance in the comprehension of exchanges mechanisms of the cell. We present a 3D reconstruction method based on a shape prior information on the observed structures and robust to shot noise and background fluorescence. A novelty with respect to the state of the art is to propose a method allowing the recovery of multiple objects aligned along the axial axis. The optimization problem can be formulated as a minimization problem where both the number of objects in the model and their parameters have to be estimated. This difficult combinatorial optimization problem is tackled by using a Marked Point Process approach which allows modelling interactions between the objects in order to regularize the inverse problem. Finally, performances of the proposed method are evaluated on synthetic data and real data.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"11 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":"132930862","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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