Classification of volumetric retinal images using overlapping decomposition and tree analysis

Methods for the classification of volumetric three-dimensional (3D) volumes (images) play an important role in the context of medical applications. In this paper, a dedicated tree based 3D representation is proposed that serves to directly capture 3D image features in such a way that classification techniques can be applied. More specifically an Overlapping Hierarchical Decomposition (OHD) technique is presented to generate a tree representation of a given 3D volume. The OHD method recursively decomposes a given 3D volume into sub-volumes forming a tree. Once the tree has been generated, a frequent sub-graph mining algorithm is applied to mine the tree representation so as to generate sub-graphs. These sub-graphs are then used to define a feature space from which feature vectors representing 3D images (one per 3D volume) can be extracted and fed into a classifier generator. To demonstrate the applicability of the proposed method a 3D Optical Coherence Tomography (OCT) retinal image screening application is considered directed at the identification of Age-related Macular Degeneration (AMD). The results show a promising performance with a best Area Under the receiver operating Curve (AUC) value of 98.7%.