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Astrocyte Analysis

Professor: Cheng-Chang Lu Subject: Image Processing. Astrocyte Analysis. By Rakesh Singrikonda [rsingrik@kent.edu] Rambabu Chelikani [rchelika@kent.edu] Manoj Thatikonda [mthatiko@kent.edu] Masters in Computer Science Kent State University. Table of contents. Astrocyte Structure

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Astrocyte Analysis

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  1. Professor: Cheng-Chang Lu Subject: Image Processing

    Astrocyte Analysis

    By RakeshSingrikonda [rsingrik@kent.edu] RambabuChelikani [rchelika@kent.edu] Manoj Thatikonda [mthatiko@kent.edu] Masters in Computer Science Kent State University
  2. Table of contents Astrocyte Structure Goals Segmentation and approaches. Our process.
  3. What is astrocyte? These are star-shaped glial cells in the brain and spinal cord. These are the most abundant cell of the human brain. Astrocytes are a sub-type of glial cells in the central nervous system.
  4. structure
  5. Goals: automatically segment Individual Glial Cells. intelligent thresholding. Segmentation. seed points classification. classify astrocytes and provide volume and surface area.
  6. Segmentation it is the process of partitioning a digital image into multiple segments. it is used to locate objects or their boundaries.
  7. Approaches to segmentation: Simple thresholding. Edge detection. Watershed transformation. Etc….
  8. Simple thresholding How To Choose The Value For The Threshold T ? By Visual Inspections Based On The Accurate Image Level The Threshold Can Be Applied. Does not require specific knowledge about the image.
  9. Edge detection Edge Detection Extracts The Boundaries Of The Objects, Instead Of The Objects Themselves. Edge detection aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Discontinuities in image brightness are likely to correspond to: discontinuities in depth discontinuities in surface orientation changes in material properties variations in scene illumination
  10. Watershed transformation Watershed Segmentation Is An Approach Developed To Solve The Very Common Problem Of Separating Touching Objects. Segmentation Failed To Separate Too Close Objects
  11. OUR Process Segmentation Thresholding, edge detection, watershed transformation
  12. Actual Image Cells after segmentation
  13. Center of mass map
  14. Centroid identification
  15. Test Results
  16. 3D identification results
  17. 3D identification results
  18. Summary of results
  19. References [1] V. Grau*, A. U. J. Mewes, M. Alcañiz, Member, IEEE, R. Kikinis, And S. K. Warfield, Member, IEEE “Improved Watershed Transform For Medical Image Segmentation Using Prior Information” Ieee Transactions On Medical Imaging, Vol. 23, No. 4, April 2004 [2] Salem Saleh Al-amri, N.V. Kalyankar And Khamitkar S.D “Image Segmentation By Using Thershod Techniques” Journal Of Computing, Volume 2, Issue 5, May 2010, Issn 2151-9617 [3] Takumi Uemura, Gou Koutaki and Keiichi Uchimura "Image Segmentation based on Edge Detection using Boundary code" International Journal of Innovative Computing, Information and Control, volume 7, Number 10, October 2011
  20. Thank You !!!
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