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The development of generative Bayesian models for classification of cell images

By A. El-Shanawany

Abstract

A generative model for shape recognition of biological cells in images is developed. The model is designed for analysing high throughput screens, and is tested on a genome wide morphology screen. The genome wide morphology screen contains order of 104 images of fluorescently stained cells with order of 102 cells per image. It was generated using automated techniques through knockdown of almost all putative genes in Drosphila melanogaster. A major step in the analysis of such a dataset is to classify cells into distinct classes: both phenotypic classes and cell cycle classes. However, the quantity of data produced presents a major time bottleneck for human analysis. Human analysis is also known to be subjective and variable. The development of a generalisable computational analysis tool is an important challenge for the field. Previously cell morphology has been characterized by automated measurement of user-defined biological features, often specific to one dataset. These methods are surveyed and discussed. Here a more ambitious approach is pursued. A novel generalisable classification method, applicable to our images, is developed and implemented. The algorithm decomposes training images into constituent patches to build Bayesian models of cell classes. The model contains probability distributions which are learnt via the Expectation Maximization algorithm. This provides a mechanism for comparing the similarity of the appearance of cell phenotypes. The method is evaluated by comparison with results of Support Vector Machines at the task of performing binary classification. This work provides the basis for clustering large sets of cell images into biologically meaningful classes

Publisher: UCL (University College London)
Year: 2010
OAI identifier: oai:eprints.ucl.ac.uk.OAI2:20314
Provided by: UCL Discovery

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Citations

  1. (2003). A Coulson, C Echeverri and N Perrimon \A functional genomic analysis of cell morphology using RNA interference"
  2. (2000). A Meijster, \The Watershed Transform: De¯nitions, Algorithms and Parallelization Strategies", Fundamenta Informaticae
  3. (2005). A multi-population algorihtm for fast and robust ellipse detection"
  4. (1994). A Multi-Resolution Algorithm For Cytological Image Segmentation", Intelligent Information
  5. (2001). A Neural Classi¯er Enabling High-Throughput Topological Analyis of Lymphocytes in Tissue Sections"
  6. A Quad Tree Approach To Image Segmentation Which Combines Statistical And Spatial Information"
  7. (1978). A Threshold Selection Method from Gray-Scale Histogram,"
  8. (1996). A Water Immersion Algorithm For Cytological Images Segmentation"
  9. (2007). AE Carpenter \CellPro¯ler: free, versatile software for automated biological image analysis"
  10. (1995). An Improved Watershed Algorithm for Counting Objects In Noisy Anisotropic 3D Biological Images"
  11. (2006). Analysis of cell-based RNAi screens",
  12. (2000). Angular Bisector Network, a Simpli¯ed Generalized Voronoi Diagram: Application to Processing Complex Interactions in Biomedical Images"
  13. (1997). Applying Watershed Algorithms to the Segmentation of Clustered Nuclei"
  14. (1989). Automated Detection and Recogntion of Live Cells in Tissue Using Image Cytometry",
  15. (1997). Automated Image Analysis Technologies for Biological 3D Light Microscopy"
  16. (2003). Automatic Quanti¯cation Of Viability In Epithelial Cell Cultures by Texture Analysis"
  17. (1997). Automatic Watershed Segmentation of Randomly Textured Color Images"
  18. (2005). Ayaka Saka, Tomoyuki Fukuda, Satoru Ishihara, Satomi Oka, Genjiro Suzuki, Machika Watanabe, Aiko Hirata, Miwaka Ohtani, Hiroshi Sawai, Nicolas Fraysse,
  19. (1992). Biological pattern recognition by neural networks"
  20. CJ Taylor \Statistical models of appearance for medical image analysis and computer vision"
  21. (2007). Clustering appearance and shape by learning jigsaws"
  22. (2005). Deriving a Hierarchical Representation of Lung Disease using ReSampling Mixture Models"
  23. (2005). Discovering objects and their location in images"
  24. (1999). Distance Transformations: Fast Algorithms and Applications to Medical Image Processing",
  25. (2004). Distinctive Image Features from Scale-Invariant Keypoints",
  26. Epitomic Analysis of Appearance and Shape,"
  27. Euclidean distance mapping"
  28. Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods"
  29. (2006). Faster Prenatal Genetic Tests: Novel Unsupervised, Automatic and Robust Methods of Segmentation of Nuclei and Probes" Lecture notes in computer science,
  30. (2006). FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets"
  31. (2006). FlyRNAi: the Drosophila RNAi screening center database"
  32. (2002). Functional genomic analysis of phagocytosis and identi¯cation of a Drosophila receptor for E. coli",
  33. (1997). Generative models for discovering sparse distributed representations"
  34. (2003). Greenspan \An E±cient Image Similarity Measure Based on Approximations of KL-Divergence Between Two Gaussian Mixtures"
  35. (1993). Hierarchical mixtures of experts and the EM algorithm",
  36. (2006). Improving Recognition of Novel Input with Similarity",
  37. Information, Inference And Learning Algorithms",
  38. (2000). Iterative Thresholding For Segmentation of Cells from Noisy Images",
  39. (2003). Latent Dirichlet Allocation"
  40. (2000). Learning Low-Level Vision"
  41. (2006). Learning Patch Dependencies For Improved Pose Mismatched Face Veri¯-cation"
  42. (2001). LIBSVM: a library for support vector machines,
  43. (1995). Live Cell Image Segmentation",
  44. (2005). Local Features for Object Class Recognition"
  45. (1976). Markovian Analysis Of Cervical Cell Images",
  46. (1977). Maximum Likelihood from Incomplete Data via the \EM" Algorithm",
  47. (2000). Medical Image Analysis: Progress over Two Decades and the Challenges Ahead",
  48. (1986). Minimum error thresholding"
  49. (1988). Mixture Models Inference And Applications To Clustering"
  50. (1992). Nonlinear total variation based noise removal algorithms"
  51. (2003). On the Accurate Counting of Tumour Cells"
  52. (1998). On The Sensitivity of the Hough Transform for Object Recognition"
  53. On The Statistical Analysis Of Dirty Pictures"
  54. (1998). Optimal Segmentation of Cell Images"
  55. (2005). Pattern Recognion Methods for Identi¯cation of Shell¯sh Larvae",
  56. (2006). Pattern Recognition And Machine Learning"
  57. (2006). Pattern Recogntion Third Edition",
  58. (2001). PCA versus LDA"
  59. Potent and speci¯c genetic interference by double stranded RNA in Caenorhabditis elegans"
  60. (1999). Probabilistic Latent Semantic Indexing",
  61. (2003). Quanti¯cation of Stretch-Induced Cytoskeletal Remodeling in Vascular Endothelial Cells by Image Processing" Cytometry Part A Vol 5,
  62. (2007). Quantitative Morphological Signatures De¯ne Local Signaling Networks Regulating Cell Morphology" Science 316,
  63. (1999). RNAi and double-strand RNA," Genes & Development,
  64. (2004). RNAi in a postmodern, postgenomic era",
  65. Sese, Yoichiro Nakatani, Fumi Sano3,4, Masashi Yukawa, Yoshikazu Ohya, Shinichi Morishita, \Data mining tools for the Saccharomyces cerevisiae morphological database"
  66. (1998). Statistical learning theory"
  67. (2000). Statistical pattern recognition: a review,"
  68. (2006). Statistical practice in high-throughput screening data analysis",
  69. (2005). The Audio Epitome: A New Representation For Modeling And Classifying Auditory Phenomena"
  70. (1992). The Euclidean Distance Transformations In
  71. (2002). The Expectation Maximization Algorithm",
  72. (2004). The functions of animal microRNAs",
  73. (2002). The Image Processing Handbook,
  74. (2004). Toward Leukocyte Recognition Using Morphometry, Texture and Color",
  75. (2001). Transformation invariant clustering and dimensionality reduction"
  76. (2005). Voronoi-Based Segmentation of Cells on Image Manifolds",
  77. (2005). WG Dougherty \Plant Pathology And RNAi: A Brief History"
  78. (2004). What energy functions can be minimised via graph cuts?"

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