1. Can Color Detect Cancer? Andrew Rabinovich 12/5/02

2. Dead or Not? E – 300% cancerous  DEADF – 0% cancerous  HEALTHY

3. How To Detect Cancer? Spectral Information Spetial Information  Texture

4. Spectral Information Analysis Proper Image Acquisition Pre-processing(image registration) Color Information Extraction

5. Image Acquisition RGB vs. Hyperspectral

6. Image Registration Registering spectral bands with each other is absolutely unavoidable!!! Acquisition system instability & optical aberrations result in spectral stack misalignment

7. Raw Spectral Data Short Band Pass (Blue) Long Band Pass (Red)

8. Misalignment

10. Registration of Multi modal Images No brightness constancy Common features at high resolution Individual features at low resolution Suppress the individual and extract the common using a high pass filter

11. Laplacian of Gaussian Filter 0.10.51 5 (-1.9694, 2.1693)(-1.7186, 2.0336)(-1.9646, 2.1624) 10 (-1.9264, 2.1329)(-1.8773, 2.1047)-1.9599 2.1592 20 (-1.8815, 2.1150)(-1.7773, 2.0511)-1.9559 2.1633 50 (-1.8809, 2.1283)(-1.7986, 2.0602)-1.9472 2.1762 Mean Shift: (-1.8970, 2.1253)

12. Filtered Images Low Band Filtered High Band Filtered

13. Shi & Tomasi Affine Registration Determine the motion based on an Affine transformation Transformation is found to sub-pixel resolution

14. Registered Spectral Images

16. Before and After

17. Color Models to Extract Spectral Signal Color Deconvolution Non-Negative Matrix Factorization Independent Components Analysis

18. Color Deconvolution

19. Non-Negative Matrix Factorization

20. ICA

21. Discussion To quantify the separation of spectral signals, each of the dies must be imaged independently and compared with the separated signal This study was done with RGB, however, Hyperspectral is a MUST