We propose a system for describing skin lesions images based on a
human perception model.
Pigmented skin lesions including melanoma and other types of skin
cancer as well as non-malignant lesions are used.
Works on classification of skin lesions already exist but they mainly
concentrate on melanoma.
The novelty of our work is that our system gives to skin lesion images a semantic label in a manner similar to humans.
This work consists of two parts: first we capture they way users
perceive each lesion, second we train a machine learning system
that simulates how people describe images.
For the first part, we choose 5 attributes: colour (light to dark),
colour uniformity (uniform to non-uniform), symmetry (symmetric to
non-symmetric), border (regular to irregular), texture (smooth to
rough). Using a web based form we asked people to pick a value of each attribute for each lesion.
In the second part, we extract 93 features from each lesions and we
trained a machine learning algorithm using such features as input and
the values of the human attributes as output.
Results are quite promising, especially for the colour related
attributes, where our system classifies over 80% of the lesions into
the same semantic classes as humans.