J Biomed Inform - Content-based histopathology image retrieval using a kernel-based semantic annotation framework.

Tópicos

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Resumo

Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image as query. The proposed approach automatically annotates the images in the collection, as well as the query images, with high-level semantic concepts. This semantic representation delivers an improved retrieval performance providing more meaningful results. We model the problem of automatic image annotation using kernel methods, resulting in a unified framework that includes: (1) multiple features for image representation, (2) a feature integration and selection mechanism (3) and an automatic semantic image annotation strategy. An extensive experimental evaluation demonstrated the effectiveness of the proposed framework to build meaningful image representations for learning and useful semantic annotations for image retrieval.

Resumo Limpo

larg amount histolog imag captur archiv patholog depart due ever expand use digit microscopi abil manag access collect digit imag regard key compon next generat medic imag system paper address problem retriev histopatholog imag larg collect use exampl imag queri propos approach automat annot imag collect well queri imag highlevel semant concept semant represent deliv improv retriev perform provid meaning result model problem automat imag annot use kernel method result unifi framework includ multipl featur imag represent featur integr select mechan automat semant imag annot strategi extens experiment evalu demonstr effect propos framework build meaning imag represent learn use semant annot imag retriev

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