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Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com BACKGROUND FLUORESCENCE ESTIMATION AND VESICLE SEGMENTATION IN LIVE CELL IMAGING WITH CONDITIONAL RANDOM FIELDS By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or nonstatic background. In this paper, we consider the problem of vesicle segmentation and time-varying background estimation at the cellular scale. The main idea is to formulate the joint segmentation-estimation problem in the general conditional random field framework. Furthermore, segmentation of vesicles and background estimation are alternatively performed by energy minimization using a min cut-max flow algorithm. The proposed approach relies on a detection measure computed from intensity contrasts between neighboring blocks in fluorescence microscopy images. This approach permits analysis of either 2D + time or 3D + time data. We demonstrate the performance of the so-called C-CRAFT through an experimental comparison with the state-of-the-art methods in fluorescence video-microscopy. We also use this method to characterize the spatial and temporal distribution of Rab6 transport carriers at the cell periphery for two different specific adhesion geometries.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: The joint advances in molecular biology and optics have revolutionized investigation methods in biology. Over the last two decades, progress in light microscopy and fluorescence molecule tagging have typically enabled to analyze transport pathways that permit exchanges between intracellular compartments. Vesicular transport carriers are mainly involved in the communications between cell compartments, connected by multiple routes. They are known to actively move on microtubules and actin filaments networks via molecular motors [1]. In live cell fluorescence microscopy images, vesicles often appear as bright spots with intensity that varies along time over a time-varying and cluttered background. Localization and morphology assessment of these small objects over time is then crucial to provide valuable information for quantitative traffic analysis. However, the manual detection of vesicles over a cluttered time-varying background is known to be tedious and subjective. Fluorescence images are relatively complex to analyze, depending on the optical setup, the properties of the detectors and the photophysics inherent to the diverse fluorescent probes. Automatic methods have the obvious advantage of being quicker and reproducible. Computational imaging methods have been developed first to robustly detect and track vesicles over a static background. Data are most often massively collected and need to be processed automatically. Accordingly, one needs to address both the methodological and computational issues involved in detecting multiple small moving subcellular objects in order to more accurately quantify membrane transport from fluorescence microscopy image data. Our biological motivation is related to the mechanisms regulating the targeting and transport of proteins to the place where they operate within the cell. In this paper, we focus on the Rab6 protein as a typical intracellular membrane-associated protein which is either: i) free in the cytosol (diffusion); ii) ii) anchored to moving transport carriers (vesicles) between the Golgi and the periphery of the cell; iii) iii) attached to the Golgi membrane Rab6 is known to promote vesicle trafficking from Golgi to Endoplasmic Reticulum or to plasma membrane. Here, we address the issue of segmenting small vesicles in the cluttered time-varying
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com cytosol. In our study, micro-fabricated patterns have been used to enforce cells to have circular or crossbow normalized shapes. Micro-patterns impose constraints on the cytoskeleton and the location of organelles (e.g. Golgi apparatus) is thus better controlled. These micro-patterns also influence the spatial distribution of Rab6 transport carriers as confirmed with probabilistic density maps as designed in, However, the direct influence of the micro-patterns on the spatial dissemination of this trafficking vesicles has so far not been completely characterized. New computational methods and appropriate image processing algorithms are then required to quantify the influence of micro-patterns on Rab6 trafficking distribution. In this paper, we present a statistical Bayesian approach in the framework of conditional random fields for background estimation and vesicle segmentation. Within this approach, we have designed a robust detection measure for fluorescence microscopy based on the distribution of neighbor patch similarity. We formulate the vesicle segmentation and background estimation as a global energy minimization problem. An iterative scheme to jointly segment vesicles and background is proposed for 2D−3D fluorescence image sequences. Preliminary ideas were described in,In this paper, we provide the following improvements and contributions: • a novel robust thresholding procedure for the patch-based detection measure; • a new spatial regularization term weighted by intensity contrasts that preserves vesicle contours; • an extension of the method devoted to the processing of 2D+time data and 3D+time data within the same framework; • a quantitative comparison with state-of-the-art methods on a large set of synthetic image sequences with a cluttered time-varying background; • a quantitative validation of the vesicle segmentation method on 2D and 3D micro- patterned cells expressing GFP-Rab6. The remainder of the paper is organized as follows. The state-of-the-art methods for particle detection and object/background separation in fluorescence microscopy imaging are presented in Section II. Section III presents our C-CRAFT(Conditional RAndom Fields for protein Transport Carriers segmentation) method which can handle cluttered time-varying background. In
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com Section IV, the performance of the C-CRAFT method is compared to existing methods on 2D synthetic and real fluorescence microscopy image sequences. Finally, we use the C-CRAFT method to quantify the spatial distribution of Rab6 transport carrier trafficking for crossbow and circular-shaped cells. Section V contains concluding remarks.
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: We have proposed an original method in the framework of conditional random fields for jointly segmenting vesicles and estimating background in fluorescence live cell microscopy. We have introduced a novel detection measure based on contrast between neighboring image patches. It allows us to effectively take into account the local context in fluorescence images. The vesicle supports and background intensities are recovered alternatively by employing a min cut-max flow minimization algorithm. The method can process both 2D+time and 3D+time data. We also have conducted an extensive experimental comparison of our method with six state-of-the-art methods for spot detection and background estimation. This comparison demonstrates the better performance of the C-CRAFT method for vesicle segmentation and background estimation in fluorescence video-microscopy with cluttered timevarying background. It can be tested online. As the C-CRAFT method relies on a detection measure based on intensity contrast, it could be applied to the segmentation of any structure in fluorescence microscopy. Nevertheless, this approach has been specifically developed for vesicle traffic analysis on a time-varying background. It would require additional experiments to evaluate the potential of the method for other applications in bioimaging. Finally, the proposed analysis of Rab6-trafficking in micropatterned cells confirmed that the vesicle trafficking concentrates at the three corner points of the crossbow-shaped cells (main adhesion sites), while the vesicle destination distribution is almost uniform for circular-shaped cells, as expected.
  10. 10. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] K. Schauer, T. Duong, C. S. Gomes-Santos, and B. Goud, “Studying intracellular trafficking pathways with probabilistic density maps,” Methods Cell Biol., vol. 118, pp. 325–343, 2013. [2] N. Phansalkar, S. More, A. Sabale, and M. Joshi, “Adaptive local thresholding for detection of nuclei in diversity stained cytology images,” in Proc. IEEE Int. Conf. Commun. Signal Process., Feb. 2011, pp. 218–220. [3] A. Raj, P. van den Bogaard, S. A. Rifkin, A. van Oudenaarden, and S. Tyagi, “Imaging individual mRNA molecules using multiple singly labeled probes,” Nature Methods, vol. 5, no. 10, pp. 877–879, 2008. [4] L. Yang et al., “An adaptive non-local means filter for denoising livecell images and improving particle detection,” J. Struct. Biol., vol. 172, no. 3, pp. 233–243, 2010. [5] N. Chenouard, I. Bloch, and J.-C. Olivo-Marin, “Multiple hypothesis tracking for cluttered biological image sequences,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2736– 2750, Nov. 2013. [6] P. Ruusuvuori et al., “Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images,” BMC Bioinformat., vol. 11, no. 1, p. 248, 2010. [7] K. Li and T. Kanade, “Nonnegative mixed-norm preconditioning for microscopy image segmentation,” in Proc. Inf. Process. Med. Imag., vol. 21. 2009, pp. 362–373. [8] D. Hu, P. Sarder, P. Ronhovde, S. Orthaus, S. Achilefu, and Z. Nussinov, “Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection—A first study,” J. Microsc., vol. 253, no. 1, pp. 54–64, 2013. [9] G. Li et al., “3D cell nuclei segmentation based on gradient flow tracking,” BMC Cell Biol., vol. 8, no. 1, p. 40, 2007. [32] M. K. Bashar, K. Komatsu, T. Fujimori, and T. J. Kobayashi, “Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images,” PLoS ONE, vol. 7, no. 5, p. 1, 2012.

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