ChhangMak

Introduction

Doctors often need to screen for oral cancer. When certain wavelengths of light are emitted into the mouth, cancerous and pre-cancerous tissues will have different fluorescent properties than healthy tissue. We can use this property to identify cancerous and pre-cancerous tissue in the mouth. However, in the mouth, emitted fluorescent light is often much weaker in magnitude than the reflected light we get from the light we put into the mouth in the first place.

Thus, we want to be able to create a light that excites the fluorescent areas of the mouth. We want to be able to detect this fluorescence, however weak the fluorescence may be relative to the reflected light, so that we can identify cancerous and pre-cancerous tissue in the mouth.

That the fluorescence is much weaker than the reflected light presents a challenge to us. We create a system where we pass a narrow bandwidth light (within the excitation spectrum of the fluorophore (a.k.a. fluorescent object) we want to measure) through a shortpass filter. The resultant light would ideally be one that does not have any light with wavelength outside of the excitation spectrum, so that we see as little of the reflected light as possible. This light is to hit the fluorophore and return to us via the camera. We pass the light heading into the camera through a longpass filter, again to allow fluorescent light through and block any reflected light.

This will allow us to detect the fluorescence in a standard camera with Bayer (red-green-green-blue) sensors. We would ideally want to present the camera's output to a doctor who can examine the fluorescence to screen for oral cancer. Thus, we want to optimize our filters and light such that the camera's output demonstrates a great amount of contrast between fluorescing and non-fluorescing parts of the image.

In this project, we use the ISETCam and ISETCamFluorescence repositories to simulate our camera, longpass filter, shortpass filter, light spectrum, and fluorophore. Using many simulations, we can find optimal cutoff wavelengths for our filters, optimal wavelengths for light emitted for each fluorophore tested.

A representation of our physical system that we simulate is produced below in the figure.

Background

The properties of fluorescence are known to us for various fluorophores, including porphyrins, NADH, FAD, etc., a few fluorophores one might expect to see in the mouth. We use their emission and excitation spectra in our simulations.

Filtering to find fluorescence is nothing new, as Edmund Optics has a line of filters called "Fluorescence Filters" that consumers can purchase. This line of filters, as well as many others, provide us with known transmittances that we can incorporate into our simulation.

We know that the sensor will interact with the fluorescent and reflected light through its Bayer filter array. That the three filters are sensitive to different wavelengths will play a part in determining what image we see at the output.

Methods

To approximate the physical system in simulation, we must have suitable models that are accurate enough to produce realistic-enough outputs on a real camera.

Modeling the Light

In the physical system, we would ideally choose a light with a narrow bandwidth. After all, a blue LED in real life has a very narrow spectrum. We choose a simple model to model our light: a gaussian with a standard deviation of 35 nanometers. We can vary the mean of this gaussian to simulate LEDs of different wavelengths and spectral distributions. Here is an example of a light spectrum we may use, with wavelength 400 nanometers and standard deviation of 35 nanometers.

We have also elected to select a few lights that come stock with ISETCam as well, in our many iterations of simulations.

Modeling the Longpass and the Shortpass Filters

Real-life shortpass and longpass filters look like so:

We can approximate that the nonlinear, sinusoidal, and nonzero parts of the transmission spectrum are constant. We can model the transition from the nonzero part to the zero part as a Gaussian distribution. We can model the zero part as just zero. Thus, we model our filters using a piecewise function: before the cutoff frequency, the transmission spectrum is equal to the maximum of the Gaussian distribution; after the cutoff frequency, the transmission spectrum is a one-sided Gaussian.

Appendix

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