The Lt-norm method is one of the widely used matching filters for adaptive multiple subtraction. When the primaries and multiples are mixed together, the L1-norm method might damage the primaries, leading to poor late...The Lt-norm method is one of the widely used matching filters for adaptive multiple subtraction. When the primaries and multiples are mixed together, the L1-norm method might damage the primaries, leading to poor lateral continuity. In this paper, we propose a constrained L1-norm method for adaptive multiple subtraction by introducing the lateral continuity constraint for the estimated primaries. We measure the lateral continuity using prediction-error filters （PEF）. We illustrate our method with the synthetic Pluto dataset. The results show that the constrained L1-norm method can simultaneously attenuate the multiples and preserve the primaries.展开更多
TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.I...TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.In this paper,we present a new tracking algorithm combined with the unique features of TIRF.Firstly a fluorescence correction procedure was processed to solve the problem of fluorescence bleaching over time.Mobile granules were then segmented from a time-lapse image stack by an adaptive background subtraction method.Kalman filter was introduced to estimate and track the granules that allowed reducing searching range and hence greater reliability in tracking process.After the tracked granules were located in x-y plane,the z-position was indirectly inferred from the changes in their intensities.In the experiments the algorithm was applied in tracking GLUT4 vesicles in living adipose cells.The results indicate that the algorithm has achieved robust estimation and tracking of the vesicles in three dimensions.展开更多
基金This work is sponsored by National Natural Science Foundation of China （No. 40874056）, Important National Science ＆ Technology Specific Projects 2008ZX05023-005-004, and the NCET Fund.Acknowledgements The authors are grateful to Liu Yang, and Zhu Sheng-wang for their constructive remarks on this manuscript.
文摘The Lt-norm method is one of the widely used matching filters for adaptive multiple subtraction. When the primaries and multiples are mixed together, the L1-norm method might damage the primaries, leading to poor lateral continuity. In this paper, we propose a constrained L1-norm method for adaptive multiple subtraction by introducing the lateral continuity constraint for the estimated primaries. We measure the lateral continuity using prediction-error filters （PEF）. We illustrate our method with the synthetic Pluto dataset. The results show that the constrained L1-norm method can simultaneously attenuate the multiples and preserve the primaries.
基金Project supported by the National Natural Science Foundation ofChina (No. 30770596)the Key Laboratory for Biomedical En-gineering of Ministry of Education of China
文摘TIRF microscopy has provided a means to view mobile granules within 100 nm in size in two dimensions.However quantitative analysis of the position and motion of those granules requires an appropriate tracking method.In this paper,we present a new tracking algorithm combined with the unique features of TIRF.Firstly a fluorescence correction procedure was processed to solve the problem of fluorescence bleaching over time.Mobile granules were then segmented from a time-lapse image stack by an adaptive background subtraction method.Kalman filter was introduced to estimate and track the granules that allowed reducing searching range and hence greater reliability in tracking process.After the tracked granules were located in x-y plane,the z-position was indirectly inferred from the changes in their intensities.In the experiments the algorithm was applied in tracking GLUT4 vesicles in living adipose cells.The results indicate that the algorithm has achieved robust estimation and tracking of the vesicles in three dimensions.
