Background: Treatment of Port-Wine Stains (PWS) suffers from the absence of a reliable real-time tool for monitoring a clinical endpoint. Response to treatment varies substantially according to blood vessel geometry. Even though optical coherence tomography (OCT) has been identified as a modality with potential to suit this need, it has not been introduced as a standard clinical monitoring tool. One reason could be that - although OCT acquires data in real-time - gigabyte data transfer, processing and communication to a clinician may impede the implementation as a clinical tool.
Objectives: We investigate whether an automated algorithm can address this problem.
Methods: Based on our understanding of pulsed dye laser treatment, we present the implementation of an unsupervised, real-time classification algorithm which uses principal components data reduction and linear discriminant analysis. We evaluate the algorithm using 96 synthesized test images and 7 clinical images.
Results: The synthesized images are classified correctly in 99.8%. The clinical images are classified correctly in 71.4%.
Conclusions: Principal components-fed linear discriminant analysis (PC-fed LDA) may be a valuable method to classify clinical images. Larger sampling numbers are required for a better training model. These results justify undertaking a study involving more patients and show that disease can be described as a function of available treatment options.