Bulged Eardrum Detection From 3D Data

Bulging is a medical characteristic of the eardrum that is crucial for the diagnosis of acute otitis media. This work proposes a novel classification method for distinguishing bulged eardrums from non-bulged ones. The method uses novel key features extracted from 3D data of the tympanic membrane, captured using a new type of otoscope, the light-field otoscope, capable of non-invasive 3D imaging of the middle ear. We first introduce a variety of geometrical and statistical descriptors (based on isocontours), and then select the most discriminative ones. Results on clinical data show that, when using the proposed feature descriptors, eardrum bulging can be automatically detected with an average accuracy of approximately 82%.