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

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

How To Detect Cancer? Spectral Information Spetial Information  Texture

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

Image Acquisition RGB vs. Hyperspectral

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

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

Misalignment

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

Laplacian of Gaussian Filter ( , )( , )( , ) 10 ( , )( , ) ( , )( , ) ( , )( , ) Mean Shift: ( , )

Filtered Images Low Band Filtered High Band Filtered

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

Registered Spectral Images

Before and After

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

Color Deconvolution

Non-Negative Matrix Factorization

ICA

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