Case Retrieval in Medical Databases by Fusing Heterogeneous Information.
Saved in:
| Title: | Case Retrieval in Medical Databases by Fusing Heterogeneous Information. |
|---|---|
| Authors: | Quellec, Gwénolé1, Lamard, Mathieu2, Cazuguel, Guy1, Roux, Christian1, Cochener, Béatrice3 |
| Source: | IEEE Transactions on Medical Imaging. 01/01/2011, Vol. 30 Issue 1, p108-118. 11p. |
| Subjects: | Bayesian field theory, Diagnostic imaging, Diabetic retinopathy, Mammograms, Probability theory, Numerical solutions to equations, Wavelets (Mathematics) |
| Abstract: | A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert–Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 \pm 0.158 and 0.821 \pm 0.177, respectively, were obtained for these two databases, which is very promising. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 57254019 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Case Retrieval in Medical Databases by Fusing Heterogeneous Information. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Quellec%2C+Gwénolé%22">Quellec, Gwénolé</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lamard%2C+Mathieu%22">Lamard, Mathieu</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Cazuguel%2C+Guy%22">Cazuguel, Guy</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Roux%2C+Christian%22">Roux, Christian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cochener%2C+Béatrice%22">Cochener, Béatrice</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Medical+Imaging%22">IEEE Transactions on Medical Imaging</searchLink>. 01/01/2011, Vol. 30 Issue 1, p108-118. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+field+theory%22">Bayesian field theory</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Diabetic+retinopathy%22">Diabetic retinopathy</searchLink><br /><searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+solutions+to+equations%22">Numerical solutions to equations</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert–Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 \pm 0.158 and 0.821 \pm 0.177, respectively, were obtained for these two databases, which is very promising. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=57254019 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TMI.2010.2063711 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 108 Subjects: – SubjectFull: Bayesian field theory Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Diabetic retinopathy Type: general – SubjectFull: Mammograms Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Numerical solutions to equations Type: general – SubjectFull: Wavelets (Mathematics) Type: general Titles: – TitleFull: Case Retrieval in Medical Databases by Fusing Heterogeneous Information. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Quellec, Gwénolé – PersonEntity: Name: NameFull: Lamard, Mathieu – PersonEntity: Name: NameFull: Cazuguel, Guy – PersonEntity: Name: NameFull: Roux, Christian – PersonEntity: Name: NameFull: Cochener, Béatrice IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 01/01/2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 02780062 Numbering: – Type: volume Value: 30 – Type: issue Value: 1 Titles: – TitleFull: IEEE Transactions on Medical Imaging Type: main |
| ResultId | 1 |
