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MRF-based Fitting of a Subdivision-based Geometric Atlas

MRF-based Fitting of a Subdivision-based Geometric Atlas. Yen Le Computational Biomedicine Lab Advisor: Prof. Ioannis A. Kakadiaris. Introduction. Problem statement: Fitting a subdivision mesh-based geometric atlas onto an image. Registration of mouse brain gene expression images.

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MRF-based Fitting of a Subdivision-based Geometric Atlas

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  1. MRF-based Fitting of a Subdivision-based Geometric Atlas Yen Le Computational Biomedicine Lab Advisor: Prof. Ioannis A. Kakadiaris

  2. Introduction • Problem statement: Fitting a subdivision mesh-based geometric atlas onto an image Registration of mouse brain gene expression images Real time Segmentation of Left Ventricle

  3. Introduction (2) • Application: to register mouse brain gene expression images • Tens thousands of genes • Keys to discovering gene functions and gene networks

  4. Introduction (3) Subdivision mesh: Position of a vertex at a higher subdivision level can be computed from positions of control points Ju et al., 2003 Subdivide GeneAtlas.org

  5. Our approach • Fitting the mesh using discrete Markov Random Fields (MRFs) with similarity-based appearance prior • Contributions: • A novel method to fit a geometric atlas using discrete MRFs • A new similarity-based appearance prior for fitting a geometric atlas

  6. An illustration example On test image

  7. An illustration example (1) Current position of the mesh

  8. An illustration example (2) Find a better position for a control point

  9. An illustration example (3) Find a better position for a control point

  10. An illustration example (4) Move the control point to the new position

  11. Fitting the subdivision mesh using discrete MRFs • A fitting solution is a configuration , where is a discrete label of the displacements for each control point • This label assignment problem is solved using MRF where an energy function defined at a higher subdivision is minimized: Geometric energy Appearance energy

  12. Appearance challenge in gene expression images • Complex appearance • The same region exhibits different intensity patterns in different images • Lack of visible edge cue for the anatomical regions Hippocampus region Image 1 Image 2 Image 3

  13. Appearance Energy A similarity-based appearance prior for geometric atlas fitting Function of dissimilarity between two image patches Test image Reference image

  14. Appearance Energy (2) Appearance Energy Vertices having high consistent appearance contribute to the appearance energy • A systematic method to identify the appearance-consistent • vertices of the subdivision mesh atlas

  15. Results The manually annotated boundaries (red) and the resulting boundaries (blue) of automatic segmentation on images of genes Chrnb4 (top) and Dscr3 (bottom).

  16. Results (2) A quantitative comparison of the mean and standard deviation of the region overlap ratio with respect to manual annotation using Dice similarity coefficient.

  17. Conclusions • A novel method to fit a mesh-based geometric atlas using discrete MRFs • A new similarity-based appearance prior for fitting a geometric atlas References Le, Y., Kurkure, U., Paragios, N., Ju, T., Carson, J., Kakadiaris, I.: Similarity-based Appearance Prior For Fitting A Subdivision Mesh In Gene Expression Images. In Proc. 15th MICCAI, Nice, France, October 1-5, 2012. Kurkure, U., Le, Y., Paragios, N., Carson, J., Ju, T., Kakadiaris, I.: Markov random field-based fitting of a subdivision-based geometric atlas. In: Proc. IEEE (ICCV), Barcelona, Spain (Nov. 6-13 2011) 2540-2547

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