| Title |
A Multimodal Search Engine for Medical Imaging Studies
|
|---|---|
| Published in |
Journal of Imaging Informatics in Medicine, August 2016
|
| DOI | 10.1007/s10278-016-9903-z |
| Pubmed ID | |
| Authors |
Eduardo Pinho, Tiago Godinho, Frederico Valente, Carlos Costa |
| Abstract |
The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 26% |
| Student > Ph. D. Student | 8 | 17% |
| Student > Doctoral Student | 6 | 13% |
| Researcher | 5 | 11% |
| Other | 4 | 9% |
| Other | 3 | 7% |
| Unknown | 8 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 37% |
| Engineering | 7 | 15% |
| Medicine and Dentistry | 6 | 13% |
| Social Sciences | 3 | 7% |
| Unknown | 13 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 August 2016.
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#20,817,194
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Outputs from Journal of Imaging Informatics in Medicine
#63
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Outputs of similar age from Journal of Imaging Informatics in Medicine
#1
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