1. Christopher Mitchell The Cooper Union Fluorescent Microscopy, Eigenobjects, and the Cellular Density Project Christopher Mitchell The Cooper Union Stevens REU 2007

2. Christopher Mitchell The Cooper Union Overview Previous Work: Cell Density & Fluorescent Microscopy Outline of Project Methodology How Eigenobjects Work Applying Eigenobjects to Nuclear Density The Future of the CDP Note to any future presenters: most of the content is in the accompanying Slide Notes

3. Christopher Mitchell The Cooper Union Fluorescent Microscopy Attach marker chemical to protein Take picture of tissue Analyze marker distribution and concentration

4. Christopher Mitchell The Cooper Union Fluorescent Microscopy Uses Examine tissue structure Identify malignant cellular growth Bind to nuclear protein to view distribution of nuclei in tissue High density indicative of pre-malignant growth

5. Christopher Mitchell The Cooper Union Example of Fluorescent Microscopy Example from webpage analyzed for this presentation:

6. Christopher Mitchell The Cooper Union Cellular Attributes Normal Cells Well-organized Moderate density Specific protein distribution Malignant Cells Chaotic arrangement High density Different protein distribution

7. Christopher Mitchell The Cooper Union Advantages of Fluorescent Microscopy Less invasive Earlier detection More precise identification

8. Christopher Mitchell The Cooper Union The Future of Fluorescent Microscopy More precise tagging of proteins to identify structures Ability to tag multiple proteins to gather more information about each cell Greater understanding of inter-cell and intra- cell structure as a cause and symptom of malignant cellular growth

9. Christopher Mitchell The Cooper Union Project Methodology Application of some fluorescent microscopy methods to photographic microscopy Primarily uses variance in cellular density and nuclear proportions between normal and malignant cells Mechanical identification of suspect samples for further human or other analysis

10. Christopher Mitchell The Cooper Union Advantages of Photographic Microscopy Easier to mechanically classify Cheaper to acquire images Human-readable with minimal training

11. Christopher Mitchell The Cooper Union Disadvantages of Photographic Microscopy Less cellular detail Fewer unique indicators of normal or malignant nature

12. Christopher Mitchell The Cooper Union Nuclear Identification Methods Wavelet method  Signal processing-based (mathematical) solution Eigencell method  Computer science (algorithmic) solution

13. Christopher Mitchell The Cooper Union The Signals Method Increase image contrast Edge detection using wavelets Count nuclei and create density array Apply statistical analysis

14. Christopher Mitchell The Cooper Union The Algorithmic Method Creating training set of eigennuclei Apply image space > eigennucleus space > image space transform, find Mean Squared Errors Identify and count cells Apply statistical analysis

15. Christopher Mitchell The Cooper Union Method Evaluation For scope of project, algorithmic method chosen Easier to code, easier to understand without a Signals background More precise even though less efficient

16. Christopher Mitchell The Cooper Union Using Eigenobjects To create a training array of eigenobjects, need to start with several training images. All images must be the same dimensions Example training set: Varied sizes and rotations, but all 24x24 pixel images

17. Christopher Mitchell The Cooper Union Using Eigenobjects 2 Next, all images packed from rectangles into rows Eigenvectors created from each row and sorted by associated eigenvalues

18. Christopher Mitchell The Cooper Union Using Eigenobjects 3 In order to streamline the process, the outer product of each row is taken and packed Yields square, symmetric matrix Each row multiplied by original image produces one eigenobject

19. Christopher Mitchell The Cooper Union Using Eigenobjects 4 Trained set of eigenobjects is complete and packed into a single array for comparison To compare an image to the training set, it must be converted to object space and back to image space. Examples of eigenfaces, eigenobjects made from faces:

20. Christopher Mitchell The Cooper Union Using Eigenobjects 5 Results of image > object > image space transformations: To determine if the image is the same type of object as training set, take Mean Squared Error (MSE) between input and output

21. Christopher Mitchell The Cooper Union Finding Objects in an Image Method can be applied to find objects in a larger image All possible subimages of training set dimensions taken, MSEs calculated Threshold-filtered to find objects

22. Christopher Mitchell The Cooper Union Applications to Nuclear Density and the CDP Using eigennuclei, center of all cells in microscope image can be found Image broken into regions, number of cells in each region found Statistical analysis to determine cancer presence

23. Christopher Mitchell The Cooper Union The Future of the CDP Optimizations  Multipass approach  Scaling/rotation Further identification metrics

24. Christopher Mitchell The Cooper Union References Cytodiagnosis of Cancer Using Acridine Orange with Fluorescent Microscopy (http://caonline.amcancersoc.org/cgi/reprint/10/4/118) ‏http://caonline.amcancersoc.org/cgi/reprint/10/4/118 New Cell Imaging Method Identifies Aggressive Cancers Early (http://www.sciencedaily.com/releases/2006/03/060307085017.htm) ‏http://www.sciencedaily.com/releases/2006/03/060307085017.htm The Cellular Density Project (http://beta.cemetech.net/projects/item.php?id=1) ‏http://beta.cemetech.net/projects/item.php?id=1 Eigenfaces Group – Algorithmics (http://www.owlnet.rice.edu/~elec301/Projects99/faces/algo.html) ‏http://www.owlnet.rice.edu/~elec301/Projects99/faces/algo.html Eigenfaces (http://www.cs.princeton.edu/~cdecoro/eigenfaces/) ‏