| Malignant melanoma poses a significant
risk to Canadians and continues to rise. Therapy for advanced melanoma
remains poor, with a five-year survival rate of 50% or less. However if
melanoma is detected early, the five-year survival rate is around 95%.
Therefore, early diagnosis of melanoma is critical so that it can be
completely excised surgically while it is still localized. Our goal is
to develop an automatic screening system for evaluating suspicious
pigmented skin lesions including both melanocytic (moles) and non-melanocytic
lesions, which can be used to help family practitioners (FPs) and other
health care professionals determine whether or not a patient should
undergo an invasive skin biopsy or be referred to a specialist
dermatologist. Additionally we hope that the system will be used by
experts as a “second reader” to help expert dermatologists to diagnose
malignant melanomas and atypical moles.
Clinical diagnostic methods involve
visual inspection and simple diagnosis based on common features:
Asymmetry, Border irregularity, Colour and Diameter (ABCD). Experts may
also examine a skin lesion under high magnification obtained using a
dermoscope (a hand-held magnifying device using polarized light), and
consider further features for classification, especially those involving
texture and textural patterns. Our goal is to automatically quantify the
textural information in skin lesions and incorporate these texture
features into an automatic diagnosis system.
We at the SFU's School of Computing
Science, Medical Image Computing Analysis Laboratory, are working
closely with the Skin Care and Dermatology Centre at UBC to achieve
these goals, through 3 major efforts:
- Developing and evaluating a novel
low-cost dermoscope system which will provide the necessary
high-resolution images for our automated diagnosis system.
- Developing machine learning and
image processing techniques to identify major features used for
classification of images
- Developing methods to
automatically, and reliably, process a large number of skin lesion
images.
|
