A Semantic Content-Based Retrieval Method for Histopathology Images

This paper proposes a model for content-based retrieval of histopathology images. The most remarkable characteristic of the proposed model is that it is able to extract high-level features that reflect the semantic content of the images. This is accomplished by a semantic mapper that maps conventional low-level features to high-level features using state-of-the-art machine-learning techniques. The semantic mapper is trained using images labeled by a pathologist. The system was tested on a collection of 1502 histopathology images and the performance assessed using standard measures. The results show an improvement from a 67% of average precision for the first result, using low-level features, to 80% of precision using high-level features.

• Type: Conference
• Year: 2008
• Where: AIRS
• Authors: Juan C. Caicedo, Fabio A. González, Eduardo Romero
• Keywords: AIRS 2008, Histopathology Images, Information Retrieval, Low-level Features, Semantic Mapper