Large Data Sets WorkshopUniversity of Nottingham

13th April 2006

Polarized Light Imaging for Skin Cancer Diagnosis

James HousleySchool of Electrical and Electronic Engineering

University of Nottingham

eexjh1@nottingham.ac.uk

Overview

Skin and polarized light Data so far Analysis methods so far Ideal data What can we do with it? Conclusion

Skin is permeable to light

Surface Reflections

Superficial Visitation

Deep Visitation

Light In

(Not to scale)

Skin is permeable to light

Light In Light Out

Polarized Light

Linear Polarization Circular Polarization

Co-polar and Cross-polar

Co-polar Cross-polar

Cross-polarCo-polarReference

Linear:

Reference

Circular:

How is polarization useful?

More collisions, less polarization maintained Deeper visitation, more collisions

Deeper visitation = less polarization maintained

Linearly polarized light on skin

Co-polar

Co-polar

Random (Co-polar + Cross-polar)

Linearly Polarized Light In

How can we use that?

Detect co-polar and cross-polar light separately

Channel 1 – channel 2:surface + superficial + deep – deep = surface + superficial

Channel Light Configuration Skin Information

1 Linear Co-polar Surface Reflections & Superficial Layers & Deep Layers

2 Linear Cross-polar Deep Layers

Linear vs. circular

Surface reflections are cross-polar in circular polarization compared to co-polar in linear polarization

For circularly polarized light, the direction of polarization is maintained, but the

direction of the light is reversed. Therefore circular polarization is ‘flipped in helicity’

by reflections

Linearly polarized light stays polarized in the same plane after reflection

Light

Polarization

Circularly polarized light on skin

Cross-polar (cf. co-polar for linear polarization)

Co-polar

Random (Co-polar + Cross-polar)

Circularly Polarized Light In

Any better?

Channel 3 – channel 2:superficial + deep – deep = superficial

Channel Light Configuration Skin Information

1 Linear Co-polar Surface Reflections & Superficial Layers & Deep Layers

2 Linear Cross-polar Deep Layers

3 Circular Co-polar Superficial Layers & Deep Layers

4 Circular Cross-polar Surface Reflections & Deep Layers

A demonstration

Channel 1 Channel 3 Channel 4Channel 2

Channel 1 – 2 Channel 3 – 2

What next?

Extract information from imagesMalignant LesionsBenign Lesions

Comparing ChannelsScattergraph - every point represents the

intensity of a pixel in two different channels

Comparing channels

Principal components analysis

Method of reducing dimensions in data Four images = four dimensions 1st principal component is an image which

contains the most possible information from all four images

Represents the best possible way of reducing the four dimensional data down to one dimension

Principal components analysis

Principal components analysis

Ideal data

4 channels 4 light sources

16 images per skin sample

Or, for superficial skin layers only

4 images per skin sample

(1 per light source)

What can we do with this data?

Principal components analysis Segmentation Neural networks

Conclusion

?

Acknowledgements

Dr. Steve Morgan Dr. John Crowe Dr. Ian Stockford

Any questions?

Channel 1 Channel 3 Channel 4Channel 2

Channel 1 – 2 Channel 3 – 2