Content-based histopathology image retrieval using a kernel-based semantic annotation framework

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 address...

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Bibliographic Details
Published in:Journal of biomedical informatics 2011-08, Vol.44 (4), p.519-528
Main Authors: Caicedo, Juan C., González, Fabio A., Romero, Eduardo
Format: Article
Language:English
Subjects:
Algorithms
Auto-annotation
Biomedical images
Carcinoma, Basal Cell - pathology
Computational Biology
Database Management Systems
Databases, Factual
Diagnostic Imaging
Histocytochemistry
Histology
Histopathology
Humans
Image Interpretation, Computer-Assisted - methods
Image retrieval
Kernel alignment
Kernels
Medical Informatics
Pattern Recognition, Automated - methods
Semantics
Skin Neoplasms - pathology
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Summary: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.
ISSN:1532-0464
1532-0480
DOI:10.1016/j.jbi.2011.01.011