Abstract: Patients with diabetes are susceptible to chronic foot
wounds which may be difficult to manage and slow to heal.
Diagnosis and treatment currently rely on the subjective judgement of
experienced professionals. An objective method of tissue assessment
is required. In this paper, a data fusion approach was taken to wound
tissue classification. The supervised Maximum Likelihood and
unsupervised Multi-Modal Expectation Maximisation algorithms
were used to classify tissues within simulated wound models by
weighting the contributions of both colour and 3D depth information.
It was found that, at low weightings, depth information could show
significant improvements in classification accuracy when compared
to classification by colour alone, particularly when using the
maximum likelihood method. However, larger weightings were
found to have an entirely negative effect on accuracy.