Supervised Content Based Image Retrieval Using Radiology Reports

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Ramos, J (Author) Other; Kockelkorn, T (Author) Other; van Ginneken, B (Author) Other; Viergever, Max (Author) Other; Ramos, R (Author) Other; Campilho, A (Author) FEUP

Conference proceedings International

Title: IMAGE ANALYSIS AND RECOGNITION, PT II

Pages: 249-258

9th International Conference on Image Analysis and Recognition (ICIAR)

Aveiro, PORTUGAL, JUN 25-27, 2012

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ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-005-K1G

DOI: 10.1007/978-3-642-31298-4_30

Abstract (EN): Content based image retrieval (CBIR) is employed in medicine to improve radiologists' diagnostic performance. Today effective medical CBIR systems are limited to specific applications, as to reduce the amount of medical knowledge to model. Although supervised approaches could ease the incorporation of medical expertise, its application is not common due to the scarce number of available user annotations. This paper introduces the application of radiology reports to supervise CBIR systems. The concept is to make use of the textual distances between reports to build a transformation in image space through a manifold learning algorithm. A comparison was made between the presented approach and non-supervised CBIR systems, using a Leave-One-Patient-Out evaluation in a database of computer tomography scans of interstitial lung diseases. Supervised CBIR augmented the mean average precision consistently with an increase between 3 to 8 points, which suggests supervision by radiology reports increases CBIR performance.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10