Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867884
R. Tournemenne, Christel Ducroz, J. Olivo-Marin, A. Dufour
Amoeboid cell motility is characterised by the emission of protrusions at the cellular surface (also known as “blebs”). Detection and counting of these protrusions is a crucial step towards the understanding of the deformation and motility machinery. We propose an automated technique to detect protrusions at the surface of cells observed in 3D fluorescence microscopy using over-complete spherical wavelets. The framework permits intuitive manipulation of wavelets on the sphere, thanks to a straightforward analogy with traditional wavelets on the plane. We illustrate detection results on a real data set of protruding cells, indicating the reliability of the method. Moreover, the flexibility of the approach makes it easily amenable to other shape analysis problems.
{"title":"3D shape analysis using overcomplete spherical wavelets: Application to BLEB detection in cell biology","authors":"R. Tournemenne, Christel Ducroz, J. Olivo-Marin, A. Dufour","doi":"10.1109/ISBI.2014.6867884","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867884","url":null,"abstract":"Amoeboid cell motility is characterised by the emission of protrusions at the cellular surface (also known as “blebs”). Detection and counting of these protrusions is a crucial step towards the understanding of the deformation and motility machinery. We propose an automated technique to detect protrusions at the surface of cells observed in 3D fluorescence microscopy using over-complete spherical wavelets. The framework permits intuitive manipulation of wavelets on the sphere, thanks to a straightforward analogy with traditional wavelets on the plane. We illustrate detection results on a real data set of protruding cells, indicating the reliability of the method. Moreover, the flexibility of the approach makes it easily amenable to other shape analysis problems.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"36 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":"133512934","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.2014.6868117
M. Radojević, Ihor Smal, W. Niessen, E. Meijering
Quantitative analysis of neuronal cell morphology from microscopic image data requires accurate reconstruction of the axonal and dendritic trees. The most critical points to be detected in this process are the bifurcations. Here we present a new method for fully automatic detection of bifurcations in microscopic images. The proposed method models the essential characteristics of bifurcations and employs fuzzy rule based reasoning to decide whether the extracted image features indicate the presence of a bifurcation. Algorithm tests on synthetic image data show high noise immunity and experiments with real fluorescence microscopy data exhibit average recall and precision of 90.4% and 90.5% respectively.
{"title":"Fuzzy logic based detection of neuron bifurcations in microscopy images","authors":"M. Radojević, Ihor Smal, W. Niessen, E. Meijering","doi":"10.1109/ISBI.2014.6868117","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868117","url":null,"abstract":"Quantitative analysis of neuronal cell morphology from microscopic image data requires accurate reconstruction of the axonal and dendritic trees. The most critical points to be detected in this process are the bifurcations. Here we present a new method for fully automatic detection of bifurcations in microscopic images. The proposed method models the essential characteristics of bifurcations and employs fuzzy rule based reasoning to decide whether the extracted image features indicate the presence of a bifurcation. Algorithm tests on synthetic image data show high noise immunity and experiments with real fluorescence microscopy data exhibit average recall and precision of 90.4% and 90.5% respectively.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"81 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":"132003463","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.2014.6867882
Wenyang Liu, D. Ruan
Image segmentation plays an important role in many medical applications. Automatic segmentation algorithms are challenged by low SNR and significant artifacts resulting from motion and signal voids. In this study, we propose a novel level set based segmentation method with a shape dictionary. Unlike previous studies that use a single template or probabilistic models, we propose to construct a shape dictionary and model the shape prior as sparse combinations of shape templates in the dictionary. The proposed method generated promising segmentation results on low SNR MR images, even with signal voids.
{"title":"Segmentation with a shape dictionary","authors":"Wenyang Liu, D. Ruan","doi":"10.1109/ISBI.2014.6867882","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867882","url":null,"abstract":"Image segmentation plays an important role in many medical applications. Automatic segmentation algorithms are challenged by low SNR and significant artifacts resulting from motion and signal voids. In this study, we propose a novel level set based segmentation method with a shape dictionary. Unlike previous studies that use a single template or probabilistic models, we propose to construct a shape dictionary and model the shape prior as sparse combinations of shape templates in the dictionary. The proposed method generated promising segmentation results on low SNR MR images, even with signal voids.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868082
W. Cong, Ge Wang
Nanophosphors emit near-infrared (NIR) light upon X-ray excitation, and can be functionalized as optical probes for in vivo molecular imaging. X-ray luminescence computed tomography (XLCT) combines the high sensitivity optical imaging with the high spatial resolution X-ray imaging to visualize specific molecular and cellular targets, pathways and therapeutic responses. In this paper, we propose an X-ray fan-beam luminescence tomography to quantify a spatial distribution of nanophosphors in a biological object. A practical imaging system is designed for the X-ray fan-beam luminescence imaging in which the X-ray tube is collimated into a fan-beam X-rays to excite nanophosphors on a cross-section of the object. The excited nanophosphors would emit NIR light to be detected on the external surface of the object. The measured NIR light signal (2D) is used to reconstruct a nanoparticle distribution (2D) on the cross-section. In this imaging mode, the dimensionality of measurable data matches to that of the nanophosphors image to be reconstructed, allowing an accurate and reliable image reconstruction. The numerical experiments are performed to demonstrate the feasibility and merits of the proposed approach.
{"title":"X-ray fan-beam luminescence tomography","authors":"W. Cong, Ge Wang","doi":"10.1109/ISBI.2014.6868082","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868082","url":null,"abstract":"Nanophosphors emit near-infrared (NIR) light upon X-ray excitation, and can be functionalized as optical probes for in vivo molecular imaging. X-ray luminescence computed tomography (XLCT) combines the high sensitivity optical imaging with the high spatial resolution X-ray imaging to visualize specific molecular and cellular targets, pathways and therapeutic responses. In this paper, we propose an X-ray fan-beam luminescence tomography to quantify a spatial distribution of nanophosphors in a biological object. A practical imaging system is designed for the X-ray fan-beam luminescence imaging in which the X-ray tube is collimated into a fan-beam X-rays to excite nanophosphors on a cross-section of the object. The excited nanophosphors would emit NIR light to be detected on the external surface of the object. The measured NIR light signal (2D) is used to reconstruct a nanoparticle distribution (2D) on the cross-section. In this imaging mode, the dimensionality of measurable data matches to that of the nanophosphors image to be reconstructed, allowing an accurate and reliable image reconstruction. The numerical experiments are performed to demonstrate the feasibility and merits of the proposed approach.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"68 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":"133235134","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.2014.6867870
A. Crimi, M. Makhinya, U. Baumann, G. Székely, O. Goksel
Information concerning central venous pressure (CVP) is crucial in clinical situations, such as cardiac failure, volume overload, and sepsis. The measurement of CVP, however, requires insertion of a catheter through a vein up a vena cava - close to the heart - with related cost and risk of complications. Peripheral venous pressure (PVP) measurement is a technique which allows indirect assessment of CVP without catheterization. However, PVP measurement is cumbersome since it requires several devices, trained medical personnel, and is difficult to perform repeatably. Aiming at an automatic venous pressure measurement system via image-processing, we introduce in this paper a robust vessel tracking algorithm fit for this purpose. The proposed algorithm addresses the challenge of tracking compressed vessels, which is essential for this venous pressure measurement technique. Given this tracking algorithm, initial PVP measurements on healthy volunteers are reported.
{"title":"Vessel tracking for ultrasound-based venous pressure measurement","authors":"A. Crimi, M. Makhinya, U. Baumann, G. Székely, O. Goksel","doi":"10.1109/ISBI.2014.6867870","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867870","url":null,"abstract":"Information concerning central venous pressure (CVP) is crucial in clinical situations, such as cardiac failure, volume overload, and sepsis. The measurement of CVP, however, requires insertion of a catheter through a vein up a vena cava - close to the heart - with related cost and risk of complications. Peripheral venous pressure (PVP) measurement is a technique which allows indirect assessment of CVP without catheterization. However, PVP measurement is cumbersome since it requires several devices, trained medical personnel, and is difficult to perform repeatably. Aiming at an automatic venous pressure measurement system via image-processing, we introduce in this paper a robust vessel tracking algorithm fit for this purpose. The proposed algorithm addresses the challenge of tracking compressed vessels, which is essential for this venous pressure measurement technique. Given this tracking algorithm, initial PVP measurements on healthy volunteers are reported.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"146 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":"132579786","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.2014.6868046
K. Martínez, Anand A. Joshi, S. Madsen, Shantanu H. Joshi, S. Karama, F. J. Román, Julio Villalón, M. Burgaleta, P. Thompson, R. Colom
Neuroimaging techniques are now widely used to understand relationships between brain features and cognitive performance. Nevertheless, studies do not always implicate the same anatomical neural networks in intellectual function. Here we used T1-weighted brain MRI scans obtained from a sample of 82 healthy young adults to study four potential sources of variability affecting the reproducibility of brain-cognition relationships: the neuroimaging protocol used, different measures of cortical gray matter, the nature of the cognitive measurement, and sample characteristics. We found that brain networks implicated in individual differences in cognition were not consistent when derived from different gray matter measures, or from different surface-based processing pipelines, even in equivalent samples of participants. Differences in the networks associated with cognition may reflect differences in the methods used to analyze them; in addition, different individuals may reach equivalent psychological goals through disparate brain networks.
{"title":"Reproducibility of brain-cognition relationships using different cortical surface-based analysis protocols","authors":"K. Martínez, Anand A. Joshi, S. Madsen, Shantanu H. Joshi, S. Karama, F. J. Román, Julio Villalón, M. Burgaleta, P. Thompson, R. Colom","doi":"10.1109/ISBI.2014.6868046","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868046","url":null,"abstract":"Neuroimaging techniques are now widely used to understand relationships between brain features and cognitive performance. Nevertheless, studies do not always implicate the same anatomical neural networks in intellectual function. Here we used T1-weighted brain MRI scans obtained from a sample of 82 healthy young adults to study four potential sources of variability affecting the reproducibility of brain-cognition relationships: the neuroimaging protocol used, different measures of cortical gray matter, the nature of the cognitive measurement, and sample characteristics. We found that brain networks implicated in individual differences in cognition were not consistent when derived from different gray matter measures, or from different surface-based processing pipelines, even in equivalent samples of participants. Differences in the networks associated with cognition may reflect differences in the methods used to analyze them; in addition, different individuals may reach equivalent psychological goals through disparate brain networks.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"16 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":"132857776","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.2014.6867896
Guanyu Yang, Yang Chen, L. Tang, H. Shu, C. Toumoulin
Cardiac CT angiography (CCTA) is widely used in the diagnosis of coronary heart disease. It can provide 4D (3D + t) sequence with high spatial and temporal resolution. Segmentation of left ventricle (LV) in 4D CCTA sequence can provide useful information for clinical practice. In this paper, we present an automatic method for LV segmentation in 4D CCTA sequence in this paper. This method mainly relies on an accurate multi-atlas registration method. Thus, we first improve the multi-atlas registration method presented by Kirişli et al. by adding an extra registration step with an estimated heart mask. Then, we use a two-stage framework based on multi-atlas registration to segment the LV in the 4D sequence. Quantitative evaluation results show that our proposed multi-atlas registration method outperforms the Kirişli's method. Finally, experimental results using two 4D CCTA sequences indicate that our method can segment LV accurately.
{"title":"Automatic left ventricle segmentation based on multiatlas registration in 4D CT images","authors":"Guanyu Yang, Yang Chen, L. Tang, H. Shu, C. Toumoulin","doi":"10.1109/ISBI.2014.6867896","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867896","url":null,"abstract":"Cardiac CT angiography (CCTA) is widely used in the diagnosis of coronary heart disease. It can provide 4D (3D + t) sequence with high spatial and temporal resolution. Segmentation of left ventricle (LV) in 4D CCTA sequence can provide useful information for clinical practice. In this paper, we present an automatic method for LV segmentation in 4D CCTA sequence in this paper. This method mainly relies on an accurate multi-atlas registration method. Thus, we first improve the multi-atlas registration method presented by Kirişli et al. by adding an extra registration step with an estimated heart mask. Then, we use a two-stage framework based on multi-atlas registration to segment the LV in the 4D sequence. Quantitative evaluation results show that our proposed multi-atlas registration method outperforms the Kirişli's method. Finally, experimental results using two 4D CCTA sequences indicate that our method can segment LV accurately.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"102 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":"122516901","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.2014.6867903
Yechiel Lamash, A. Fischer, J. Lessick
Cardiac pathologies are generally associated with regional ventricular dysfunction. Methods for estimating the regional myocardial motion from cardiac CT image data generally ignore the rotational velocities. Reasons for this include the challenges of sparse image deformation clues, low SNR and the low temporal resolution. In the current study we propose a fast algorithm for evaluating the mechanical function of the left ventricle from cardiac CT data. A compact parametric motion model is used to describe the regional 3D contraction and twist. The algorithm is based on regularized multi-homography image registration. The rotational velocities are estimated and compared to their respective values in the literature, with good agreement. Good performance in classifying the segments as normal or abnormal with respect to expert's visual scores is obtained.
{"title":"Fast algorithm for estimating the regional mechanical function of the left ventricle from 4D cardiac CT data","authors":"Yechiel Lamash, A. Fischer, J. Lessick","doi":"10.1109/ISBI.2014.6867903","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867903","url":null,"abstract":"Cardiac pathologies are generally associated with regional ventricular dysfunction. Methods for estimating the regional myocardial motion from cardiac CT image data generally ignore the rotational velocities. Reasons for this include the challenges of sparse image deformation clues, low SNR and the low temporal resolution. In the current study we propose a fast algorithm for evaluating the mechanical function of the left ventricle from cardiac CT data. A compact parametric motion model is used to describe the regional 3D contraction and twist. The algorithm is based on regularized multi-homography image registration. The rotational velocities are estimated and compared to their respective values in the literature, with good agreement. Good performance in classifying the segments as normal or abnormal with respect to expert's visual scores is obtained.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867910
T. Becker, W. Kanje, D. Rapoport, Konstantin Thierbach, N. Scherf, Ingo Röder, A. M. Mamlouk
Mitosis detection poses a major challenge in cell tracking as mitoses are crucial events in the construction of genealogical trees. Making use of typical mitotic patterns that can be seen in phase contrast images of time lapse experiments, we propose a new benchmark data set CeTReS.B-MI consisting of mitotic and non-mitotic cells from the publicly accessible, fully labeled data set CeTReS.B. Using this data, two simple mitosis detectors (based on compactness and intensity) are used exemplarily to train, test and compare their ability to detect mitotic events. As a gold standard, we propose a linear support vector machine (SVM), which is able to separate the classes with a high accuracy (AUC=0.993). To illustrate the potential impact of a robust mitosis detection, the proposed classifiers are combined with two state of the art cell tracking algorithms. For both algorithms, performance does change when adding mitosis detection. Finally, this evaluation also emphasizes how easy implementation and comparison becomes, having suitable benchmark data at hand.
{"title":"The benchmark data SET CeTReS.B-MI for in vitro mitosis detection","authors":"T. Becker, W. Kanje, D. Rapoport, Konstantin Thierbach, N. Scherf, Ingo Röder, A. M. Mamlouk","doi":"10.1109/ISBI.2014.6867910","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867910","url":null,"abstract":"Mitosis detection poses a major challenge in cell tracking as mitoses are crucial events in the construction of genealogical trees. Making use of typical mitotic patterns that can be seen in phase contrast images of time lapse experiments, we propose a new benchmark data set CeTReS.B-MI consisting of mitotic and non-mitotic cells from the publicly accessible, fully labeled data set CeTReS.B. Using this data, two simple mitosis detectors (based on compactness and intensity) are used exemplarily to train, test and compare their ability to detect mitotic events. As a gold standard, we propose a linear support vector machine (SVM), which is able to separate the classes with a high accuracy (AUC=0.993). To illustrate the potential impact of a robust mitosis detection, the proposed classifiers are combined with two state of the art cell tracking algorithms. For both algorithms, performance does change when adding mitosis detection. Finally, this evaluation also emphasizes how easy implementation and comparison becomes, having suitable benchmark data at hand.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"212 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":"114759310","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.2014.6867978
D. Sorokin, Marco Tektonidis, K. Rohr, P. Matula
In live cell imaging it is essential to analyze the pure motion of sub-nuclear proteins without influence of the cell nucleus motion and deformation which is referred to as nucleus global motion. In this work, we propose a 2D contour-based image registration approach for compensation of the global motion of the nucleus. Compared to a previous contour-based approach, our approach employs an explicit rigid registration step to compensate the nucleus translation and rotation, it uses morphological contour matching for establishing more reliable correspondences between contours in consecutive frames, and utilizes the Navier equation for more realistically modeling the nucleus deformation. Our approach was successfully applied to real live cell microscopy image sequences and an experimental comparison with an existing contour-based registration method and an intensity-based registration method has been performed.
{"title":"Non-rigid contour-based temporal registration of 2D cell nuclei images using the Navier equation","authors":"D. Sorokin, Marco Tektonidis, K. Rohr, P. Matula","doi":"10.1109/ISBI.2014.6867978","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867978","url":null,"abstract":"In live cell imaging it is essential to analyze the pure motion of sub-nuclear proteins without influence of the cell nucleus motion and deformation which is referred to as nucleus global motion. In this work, we propose a 2D contour-based image registration approach for compensation of the global motion of the nucleus. Compared to a previous contour-based approach, our approach employs an explicit rigid registration step to compensate the nucleus translation and rotation, it uses morphological contour matching for establishing more reliable correspondences between contours in consecutive frames, and utilizes the Navier equation for more realistically modeling the nucleus deformation. Our approach was successfully applied to real live cell microscopy image sequences and an experimental comparison with an existing contour-based registration method and an intensity-based registration method has been performed.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"13 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":"115171487","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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