ImageCLEF 2009 Medical Image Annotation Task: PCTs for Hierarchical Multi-Label Classification

In this paper, we describe an approach to the automatic medical image annotation task of the 2009 CLEF cross-language image retrieval campaign (ImageCLEF). This work focuses on the process of feature extraction from radiological images and their hierarchical multi-label classification. To extract features from the images we use two different techniques: edge histogram descriptor (EHD) and Scale Invariant Feature Transform (SIFT) histogram. To annotate the images, we use predictive clustering trees (PCTs) which are able to handle target concepts that are organized in a hierarchy, i.e., perform hierarchical multi-label classification. Furthermore, we construct ensembles (Bagging and Random Forests) that use PCTs as base classifiers: this improves the predictive/ classification performance.