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Content-based histopathology image retrieval using CometCloud
BACKGROUND: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increa...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161917/ https://www.ncbi.nlm.nih.gov/pubmed/25155691 http://dx.doi.org/10.1186/1471-2105-15-287 |
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author | Qi, Xin Wang, Daihou Rodero, Ivan Diaz-Montes, Javier Gensure, Rebekah H Xing, Fuyong Zhong, Hua Goodell, Lauri Parashar, Manish Foran, David J Yang, Lin |
author_facet | Qi, Xin Wang, Daihou Rodero, Ivan Diaz-Montes, Javier Gensure, Rebekah H Xing, Fuyong Zhong, Hua Goodell, Lauri Parashar, Manish Foran, David J Yang, Lin |
author_sort | Qi, Xin |
collection | PubMed |
description | BACKGROUND: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. RESULTS: The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. CONCLUSIONS: In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms. |
format | Online Article Text |
id | pubmed-4161917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41619172014-09-13 Content-based histopathology image retrieval using CometCloud Qi, Xin Wang, Daihou Rodero, Ivan Diaz-Montes, Javier Gensure, Rebekah H Xing, Fuyong Zhong, Hua Goodell, Lauri Parashar, Manish Foran, David J Yang, Lin BMC Bioinformatics Research Article BACKGROUND: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. RESULTS: The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. CONCLUSIONS: In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms. BioMed Central 2014-08-26 /pmc/articles/PMC4161917/ /pubmed/25155691 http://dx.doi.org/10.1186/1471-2105-15-287 Text en © Qi et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Qi, Xin Wang, Daihou Rodero, Ivan Diaz-Montes, Javier Gensure, Rebekah H Xing, Fuyong Zhong, Hua Goodell, Lauri Parashar, Manish Foran, David J Yang, Lin Content-based histopathology image retrieval using CometCloud |
title | Content-based histopathology image retrieval using CometCloud |
title_full | Content-based histopathology image retrieval using CometCloud |
title_fullStr | Content-based histopathology image retrieval using CometCloud |
title_full_unstemmed | Content-based histopathology image retrieval using CometCloud |
title_short | Content-based histopathology image retrieval using CometCloud |
title_sort | content-based histopathology image retrieval using cometcloud |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161917/ https://www.ncbi.nlm.nih.gov/pubmed/25155691 http://dx.doi.org/10.1186/1471-2105-15-287 |
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