Work

Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library

Year: 2013

Type: article

Abstract: BACKGROUND: Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. OBJECTIVE: To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. METHODS: As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. RESULTS: Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. CONCLUSIONS: The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.

Source: Journal of the American Medical Informatics Association

DOI: 10.1136/amiajnl-2012-001380

Authors: Yang Chen, Xiaofeng Ren, Guo-Qiang Zhang, Rong Xu

Institutions: Case Western Reserve University, Intel (United States)

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Case Western Reserve University, National Institutes of Health, National Center for Research Resources, National Center for Advancing Translational Sciences