PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles.
In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.
Department of Computer Science, University of São Paulo at São Carlos, SP, Brazil. Electronic address: pbugatti@gmail.com.
Pedro H
PH
Bugatti
Department of Computer Science, University of Londrina, Londrina, PR, Brazil.
Daniel S
DS
Kaster
Department of Computer Science, University of São Paulo at São Carlos, SP, Brazil.
Marcelo
M
Ponciano-Silva
Department of Computer Science, University of São Paulo at São Carlos, SP, Brazil.
Caetano
C
Traina
School of Medicine, University of São Paulo at Ribeirão Preto, SP, Brazil.
Paulo M
PM
Azevedo-Marques
Department of Computer Science, University of São Paulo at São Carlos, SP, Brazil.
Agma J M
AJ
Traina
D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016247:Information Storage and Retrieval / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D008492:Medical Informatics Computing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011859:Radiography
CBIR; Distance functions; Medical images; Perceptual similarity; User profiles