INEB
INEB
TitleSupervised content based image retrieval using radiology reports
Publication TypeBook
Year of Publication2012
AuthorsRamos, J, Kockelkorn, T, Van Ginneken, B, Viergever, MA, Ramos, R, Campilho, A
Series TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.
Volume7325 LNCS
Number of Pages249 - 258
CityAveiro
ISBN Number03029743 (ISSN); 9783642312977 (ISBN)
KeywordsCBIR system, Computer tomography, Content based retrieval, Content-Based Image Retrieval, Diagnostic performance, Image analysis, Image space, Interstitial lung disease, Learning algorithms, Manifold learning algorithm, Medical computing, Medical knowledge, Radiation, Radiology, Radiology reports, User annotations
AbstractContent 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. © 2012 Springer-Verlag.
URLhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84864135039&partnerID=40&md5=1aafa2e679e79b57baf3cdb50c5e4c97