ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval,high-performance computing,and grid technology |
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| Authors: | David J Foran Lin Yang Wenjin Chen Jun Hu Lauri A Goodell Michael Reiss Fusheng Wang Tahsin Kurc Tony Pan Ashish Sharma Joel H Saltz |
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| Affiliation: | 1.Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA;2.The Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey, New Brunswick, New Jersey, USA;3.Center for Comprehensive Informatics, Emory University School of Medicine, Atlanta, Georgia, USA;4.Department of Biomedical Engineering, Emory University, Atlanta, Georgia, USA |
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| Abstract: | ![]()
Objective and designThe design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated.ResultsThe experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples. |
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| Keywords: | Computer-assisted diagnosis biomedical imaging informatics tissue microarray content-based image retrieval bioinformatics data management data integration RFID temporal database spatial database Emory biomedical informatics imaging high end computing middleware pathology |
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