Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192994
Q. Wei, S. Sueda, Joel Miller, J. Demer, D. Pai
Understanding the mechanisms of eye movement is difficult without a realistic biomechanical model. We present an efficient and robust computational framework for building subject-specific models of the orbit from magnetic resonance images (MRIs). We reconstruct three-dimensional geometric models of the major structures of the orbit (six extraocular muscles, orbital wall, optic nerve, and globe) by fitting a template to the MRIs of individual subjects. A generic template captures the anatomical properties of these orbital structures and serves as the prior knowledge to improve the completeness and robustness of the model reconstruction. We develop an automatic fitting process, which combines parametric surface fitting with successive image feature selections. Reconstructed orbit models from different subjects are demonstrated. The accuracy of the proposed method is validated through comparison of reconstructed extraocular muscle cross sections with manual segmentation. The Dice coefficient is used as the metric and good agreement is observed.
{"title":"Template-based reconstruction of human extraocular muscles from magnetic resonance images","authors":"Q. Wei, S. Sueda, Joel Miller, J. Demer, D. Pai","doi":"10.1109/ISBI.2009.5192994","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192994","url":null,"abstract":"Understanding the mechanisms of eye movement is difficult without a realistic biomechanical model. We present an efficient and robust computational framework for building subject-specific models of the orbit from magnetic resonance images (MRIs). We reconstruct three-dimensional geometric models of the major structures of the orbit (six extraocular muscles, orbital wall, optic nerve, and globe) by fitting a template to the MRIs of individual subjects. A generic template captures the anatomical properties of these orbital structures and serves as the prior knowledge to improve the completeness and robustness of the model reconstruction. We develop an automatic fitting process, which combines parametric surface fitting with successive image feature selections. Reconstructed orbit models from different subjects are demonstrated. The accuracy of the proposed method is validated through comparison of reconstructed extraocular muscle cross sections with manual segmentation. The Dice coefficient is used as the metric and good agreement is observed.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193106
Renaud Lopes, M. Vermandel, A. Dewalle-Vignion, S. Maouche, N. Betrouni
Fractal geometry may be an efficient tool for texture analysis in medical imaging. However its application is primarily restricted to 2D cases and at the only use of an approximation method of the fractal dimension (FD). Recently, multifractal analysis has showed interesting results in this field. This study focuses on the use of an optimized set of 3D fractal and multifractal features for the epileptogenic focus characterization in SPECT imaging. Our results showed that this optimized set, compared to various texture features, improved the classification rate by Support Vector Machines (SVM). Moreover, results were significantly better than the clinical method: SISCOM (Substraction Ictal SPECT Co-registred to MRI).
{"title":"An optimized set of 3D fractal and multifractal features for the epileptogenic focus characterization in SPECT imaging","authors":"Renaud Lopes, M. Vermandel, A. Dewalle-Vignion, S. Maouche, N. Betrouni","doi":"10.1109/ISBI.2009.5193106","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193106","url":null,"abstract":"Fractal geometry may be an efficient tool for texture analysis in medical imaging. However its application is primarily restricted to 2D cases and at the only use of an approximation method of the fractal dimension (FD). Recently, multifractal analysis has showed interesting results in this field. This study focuses on the use of an optimized set of 3D fractal and multifractal features for the epileptogenic focus characterization in SPECT imaging. Our results showed that this optimized set, compared to various texture features, improved the classification rate by Support Vector Machines (SVM). Moreover, results were significantly better than the clinical method: SISCOM (Substraction Ictal SPECT Co-registred to MRI).","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125437811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193186
A. Basavanhally, Jun Xu, S. Ganesan, A. Madabhushi
The current gold standard for predicting disease survival and outcome for lymph node-negative, estrogen receptor-positive breast cancer (LN-, ER+ BC) patients is via the gene-expression based assay, Oncotype DX. In this paper, we present a novel computer-aided prognosis (CAP) scheme that employs quantitatively derived image information to predict patient outcome analogous to the Oncotype DX Recurrence Score (RS), with high RS implying poor outcome and vice versa. While digital pathology has made tissue specimens amenable to computer-aided diagnosis (CAD) for disease detection, our CAP scheme is the first of its kind for predicting disease outcome and patient survival. Since cancer grade is known to be correlated to disease outcome, low grade implying good outcome and vice versa, our CAP scheme captures quantitative image features that are reflective of BC grade. Our scheme involves first semi-automatically detecting BC nuclei via an Expectation Maximization driven algorithm. Using the nuclear centroids, two graphs (Delaunay Triangulation and Minimum Spanning Tree) are constructed and a total of 12 features are extracted from each image. A non-linear dimensionality reduction scheme, Graph Embedding, projects the image-derived features into a low-dimensional space, and a Support Vector Machine classifies the BC images in the reduced dimensional space. On a cohort of 37 samples, and for 100 trials of 3-fold randomized cross-validation, the SVM yielded a mean accuracy of 84.15% in distinguishing samples with low and high RS and 84.12% in distinguishing low and high grade BC. The projection of the high-dimensional image feature data to a 1D line for all BC samples via GE shows a clear separation between, low, intermediate, and high BC grades, which in turn shows high correlation with low, medium, and high RS. The results suggest that our image-based CAP scheme might provide a cheaper alternative to Oncotype DX in predicting BC outcome.
{"title":"Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with Oncotype DX assay","authors":"A. Basavanhally, Jun Xu, S. Ganesan, A. Madabhushi","doi":"10.1109/ISBI.2009.5193186","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193186","url":null,"abstract":"The current gold standard for predicting disease survival and outcome for lymph node-negative, estrogen receptor-positive breast cancer (LN-, ER+ BC) patients is via the gene-expression based assay, Oncotype DX. In this paper, we present a novel computer-aided prognosis (CAP) scheme that employs quantitatively derived image information to predict patient outcome analogous to the Oncotype DX Recurrence Score (RS), with high RS implying poor outcome and vice versa. While digital pathology has made tissue specimens amenable to computer-aided diagnosis (CAD) for disease detection, our CAP scheme is the first of its kind for predicting disease outcome and patient survival. Since cancer grade is known to be correlated to disease outcome, low grade implying good outcome and vice versa, our CAP scheme captures quantitative image features that are reflective of BC grade. Our scheme involves first semi-automatically detecting BC nuclei via an Expectation Maximization driven algorithm. Using the nuclear centroids, two graphs (Delaunay Triangulation and Minimum Spanning Tree) are constructed and a total of 12 features are extracted from each image. A non-linear dimensionality reduction scheme, Graph Embedding, projects the image-derived features into a low-dimensional space, and a Support Vector Machine classifies the BC images in the reduced dimensional space. On a cohort of 37 samples, and for 100 trials of 3-fold randomized cross-validation, the SVM yielded a mean accuracy of 84.15% in distinguishing samples with low and high RS and 84.12% in distinguishing low and high grade BC. The projection of the high-dimensional image feature data to a 1D line for all BC samples via GE shows a clear separation between, low, intermediate, and high BC grades, which in turn shows high correlation with low, medium, and high RS. The results suggest that our image-based CAP scheme might provide a cheaper alternative to Oncotype DX in predicting BC outcome.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128263056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193046
F. Luisier, C. Vonesch, T. Blu, M. Unser
We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appealing properties, independent unbiased MSE estimates can be derived for each subband. Based on these Poisson unbiased MSE estimates, we then optimize linearly parametrized interscale thresholding. Correlations between adjacent images of the multidimensional data are accounted for through a sliding window approach. Experiments on simulated and real data show that the proposed solution is qualitatively similar to a state-of-the-art multiscale method, while being orders of magnitude faster.
{"title":"Fast Haar-wavelet denoising of multidimensional fluorescence microscopy data","authors":"F. Luisier, C. Vonesch, T. Blu, M. Unser","doi":"10.1109/ISBI.2009.5193046","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193046","url":null,"abstract":"We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appealing properties, independent unbiased MSE estimates can be derived for each subband. Based on these Poisson unbiased MSE estimates, we then optimize linearly parametrized interscale thresholding. Correlations between adjacent images of the multidimensional data are accounted for through a sliding window approach. Experiments on simulated and real data show that the proposed solution is qualitatively similar to a state-of-the-art multiscale method, while being orders of magnitude faster.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128515457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193123
Aurobrata Ghosh, Maher Moakher, R. Deriche
In Diffusion Magnetic Resonance Imaging (D-MRI), the 2nd order diffusion tensor has given rise to a widely used tool — Diffusion Tensor Imaging (DTI). However, it is known that DTI is limited to a single prominent diffusion direction and is inaccurate in regions of complex fiber structures such as crossings. Various other approaches have been introduced to recover such complex tissue micro-geometries, one of which is Higher Order Cartesian Tensors. Estimating a positive diffusion function has also been emphasised mathematically, since diffusion is a physical quantity. Recently there have been efforts to estimate 4th order diffusion tensors from Diffusion Weighted Images (DWIs), which are capable of describing crossing configurations with the added property of a positive diffusion function. We take up one such, the Ternary Quartic approach, and reformulate the estimation equation to facilitate the estimation of the non-negative 4th order diffusion tensor. With our modified approach we test on synthetic, phantom and real data and confirm previous results.
{"title":"Ternary Quartic approach for positive 4th order diffusion tensors revisited","authors":"Aurobrata Ghosh, Maher Moakher, R. Deriche","doi":"10.1109/ISBI.2009.5193123","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193123","url":null,"abstract":"In Diffusion Magnetic Resonance Imaging (D-MRI), the 2nd order diffusion tensor has given rise to a widely used tool — Diffusion Tensor Imaging (DTI). However, it is known that DTI is limited to a single prominent diffusion direction and is inaccurate in regions of complex fiber structures such as crossings. Various other approaches have been introduced to recover such complex tissue micro-geometries, one of which is Higher Order Cartesian Tensors. Estimating a positive diffusion function has also been emphasised mathematically, since diffusion is a physical quantity. Recently there have been efforts to estimate 4th order diffusion tensors from Diffusion Weighted Images (DWIs), which are capable of describing crossing configurations with the added property of a positive diffusion function. We take up one such, the Ternary Quartic approach, and reformulate the estimation equation to facilitate the estimation of the non-negative 4th order diffusion tensor. With our modified approach we test on synthetic, phantom and real data and confirm previous results.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130145617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193255
O. Dzyubachyk, W. A. Cappellen, J. Essers, W. Niessen, E. Meijering
The ultimate aim of many live-cell fluorescence microscopy imaging experiments is the quantitative analysis of the spatial structure and temporal behavior of intracellular objects. This requires finding the precise geometrical correspondence between the time frames for each individual cell and performing intracellular segmentation. In a previous paper we have developed a powerful multi-level-set based algorithm for automated cell segmentation and tracking of many cells in time-lapse images. In this paper, we propose approaches to exploit the output of this algorithm for the subsequent tasks of cell motion correction and intracellular segmentation. Both tasks are formulated as energy minimization problems and are solved efficiently and effectively by distance-transform and graph-cut based algorithms. The potential of the proposed approaches for intracellular analysis is demonstrated by successful experiments on biological image data showing PCNA-foci and nucleoli in HeLa cells.
{"title":"Energy minimization methods for cell motion correction and intracellular analysis in live-cell fluorescence microscopy","authors":"O. Dzyubachyk, W. A. Cappellen, J. Essers, W. Niessen, E. Meijering","doi":"10.1109/ISBI.2009.5193255","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193255","url":null,"abstract":"The ultimate aim of many live-cell fluorescence microscopy imaging experiments is the quantitative analysis of the spatial structure and temporal behavior of intracellular objects. This requires finding the precise geometrical correspondence between the time frames for each individual cell and performing intracellular segmentation. In a previous paper we have developed a powerful multi-level-set based algorithm for automated cell segmentation and tracking of many cells in time-lapse images. In this paper, we propose approaches to exploit the output of this algorithm for the subsequent tasks of cell motion correction and intracellular segmentation. Both tasks are formulated as energy minimization problems and are solved efficiently and effectively by distance-transform and graph-cut based algorithms. The potential of the proposed approaches for intracellular analysis is demonstrated by successful experiments on biological image data showing PCNA-foci and nucleoli in HeLa cells.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123751993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193105
Raja' S. Alomari, Jason J. Corso, V. Chaudhary, G. Dhillon
Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment.
{"title":"Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model","authors":"Raja' S. Alomari, Jason J. Corso, V. Chaudhary, G. Dhillon","doi":"10.1109/ISBI.2009.5193105","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193105","url":null,"abstract":"Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115946333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192983
D. E. Freund, N. Bressler, P. Burlina
Age related macular degeneration (AMD) is a condition of the retina that occurs with individuals over 50. AMD is characterized by the formation of drusen in the macula. This condition leads to a deterioration of foveal vision and eventually functional blindness. Automatically screening atrisk individuals may allow the detection of intermediate stage AMD where it is still treatable using anti-VEGH therapy. One of the difficulties in detecting and locating drusen is that their aspect (shape, texture, color, extent) varies significantly, and because of this it is often difficult to build a classifier. To address this difficulty we use a two pronged approach based on (a) multiscale analysis and (b) kernel based anomaly detection. We show experimental results on examples of fundus images taken from healthy and affected patients.
{"title":"Automated detection of drusen in the macula","authors":"D. E. Freund, N. Bressler, P. Burlina","doi":"10.1109/ISBI.2009.5192983","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192983","url":null,"abstract":"Age related macular degeneration (AMD) is a condition of the retina that occurs with individuals over 50. AMD is characterized by the formation of drusen in the macula. This condition leads to a deterioration of foveal vision and eventually functional blindness. Automatically screening atrisk individuals may allow the detection of intermediate stage AMD where it is still treatable using anti-VEGH therapy. One of the difficulties in detecting and locating drusen is that their aspect (shape, texture, color, extent) varies significantly, and because of this it is often difficult to build a classifier. To address this difficulty we use a two pronged approach based on (a) multiscale analysis and (b) kernel based anomaly detection. We show experimental results on examples of fundus images taken from healthy and affected patients.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115968765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192984
Sang Ho Lee, Jong Hyo Kim, J. Park, Y. Jung, W. Moon
This study was designed to characterize the spatio-temporal properties of intratumoral enhancement patterns by using voxel-wise temporal enhancement spectra and morphometry of their spatial distributions in dynamic contrast-enhanced (DCE) breast MRI. Discrete Fourier transformation (DFT) and singular value decomposition (SVD) were used to extract the temporal enhancement features for comparison, generating 4D spectral maps. The spatial variations of DFT and SVD-based eigen spectra within tumor were captured by 3D moment descriptors, respectively. Differentiation between benign and malignant tumors was carried out using least squares support vector machine (LS-SVM) with a radial basis function (RBF) kernel and leave-one-out cross validation was used for performance evaluation. Using DFT, the sensitivity, specificity and area under ROC curve were 84.8%, 64.4% and 0.728. Using SVD, the corresponding values were 100%, 86.7% and 0.935. Combination of SVD and 3D moments yields higher performance in tumor differentiation than that of DFT and 3D moments.
{"title":"Characterizing time-intensity curves for spectral morphometric analysis of intratumoral enhancement patterns in breast DCE-MRI: Comparison between differentiation performance of temporal model parameters based on DFT and SVD","authors":"Sang Ho Lee, Jong Hyo Kim, J. Park, Y. Jung, W. Moon","doi":"10.1109/ISBI.2009.5192984","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192984","url":null,"abstract":"This study was designed to characterize the spatio-temporal properties of intratumoral enhancement patterns by using voxel-wise temporal enhancement spectra and morphometry of their spatial distributions in dynamic contrast-enhanced (DCE) breast MRI. Discrete Fourier transformation (DFT) and singular value decomposition (SVD) were used to extract the temporal enhancement features for comparison, generating 4D spectral maps. The spatial variations of DFT and SVD-based eigen spectra within tumor were captured by 3D moment descriptors, respectively. Differentiation between benign and malignant tumors was carried out using least squares support vector machine (LS-SVM) with a radial basis function (RBF) kernel and leave-one-out cross validation was used for performance evaluation. Using DFT, the sensitivity, specificity and area under ROC curve were 84.8%, 64.4% and 0.728. Using SVD, the corresponding values were 100%, 86.7% and 0.935. Combination of SVD and 3D moments yields higher performance in tumor differentiation than that of DFT and 3D moments.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116036883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193276
V. Ntziachristos
Multi-spectral optoacoustic tomography (MSOT) is an imaging method that can resolve fluorochromes and other reporter molecules with high sensitivity and specificity deep inside live animals and tissues. The method can penetrate for several millimeters to centimeters in tissues and offers resolutions in the 30 – 100 micron range. We discuss advances with technical implementation and in-vivo imaging applications ranging to imaging various cellular functions in mice. MSOT comes with significant potential to shift the paradigm of biological imaging and become a method of choice in basic and drug discovery and in several clinical applications.
{"title":"Advancing biological imaging with multi-spectral optoacoustic tomography (MSOT)","authors":"V. Ntziachristos","doi":"10.1109/ISBI.2009.5193276","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193276","url":null,"abstract":"Multi-spectral optoacoustic tomography (MSOT) is an imaging method that can resolve fluorochromes and other reporter molecules with high sensitivity and specificity deep inside live animals and tissues. The method can penetrate for several millimeters to centimeters in tissues and offers resolutions in the 30 – 100 micron range. We discuss advances with technical implementation and in-vivo imaging applications ranging to imaging various cellular functions in mice. MSOT comes with significant potential to shift the paradigm of biological imaging and become a method of choice in basic and drug discovery and in several clinical applications.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122448206","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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