Automated Eardrum Registration From Light-Field Data

The performance of automated classification algorithms for medical images needs to be very high and especially robust in order to be adopted into healthcare. Most of the time the main challenge is unregistered data, since it is usually captured: 1) from different patients, 2) with different devices, and 3) at different time. Registration and normalization of the captured data is a necessary condition for success. In this paper we present for the first time an automated method to register eardrums from light-field data. This procedure uses the shape information captured by a light-field otoscope and compensates for the natural tilt of the eardrum, its size, and the camera viewpoint. Results on clinical data show that the proposed algorithm is robust and works well for different types of ear conditions.