Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs.
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| Title: | Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs. |
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| Authors: | Quellec, Gwénolé1,2 mathieu.lamard@univ-brest.fr, Lamard, Mathieu2,3, Josselin, Pierre Marie2,3,4, Cazuguel, Guy1,3, Cochener, Beatrice2,3,4, Roux, Christian1,3 |
| Source: | IEEE Transactions on Medical Imaging. Sep2008, Vol. 27 Issue 9, p1230-1241. 12p. 4 Black and White Photographs, 3 Diagrams, 6 Charts, 4 Graphs. |
| Subjects: | Aneurysms, Vascular diseases, Retina, Retina abnormalities, Genetic algorithms |
| Abstract: | In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 34386237 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Quellec%2C+Gwénolé%22">Quellec, Gwénolé</searchLink><relatesTo>1,2</relatesTo><i> mathieu.lamard@univ-brest.fr</i><br /><searchLink fieldCode="AR" term="%22Lamard%2C+Mathieu%22">Lamard, Mathieu</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Josselin%2C+Pierre+Marie%22">Josselin, Pierre Marie</searchLink><relatesTo>2,3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Cazuguel%2C+Guy%22">Cazuguel, Guy</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cochener%2C+Beatrice%22">Cochener, Beatrice</searchLink><relatesTo>2,3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Roux%2C+Christian%22">Roux, Christian</searchLink><relatesTo>1,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Medical+Imaging%22">IEEE Transactions on Medical Imaging</searchLink>. Sep2008, Vol. 27 Issue 9, p1230-1241. 12p. 4 Black and White Photographs, 3 Diagrams, 6 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Aneurysms%22">Aneurysms</searchLink><br /><searchLink fieldCode="DE" term="%22Vascular+diseases%22">Vascular diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Retina%22">Retina</searchLink><br /><searchLink fieldCode="DE" term="%22Retina+abnormalities%22">Retina abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TMI.2008.920619 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1230 Subjects: – SubjectFull: Aneurysms Type: general – SubjectFull: Vascular diseases Type: general – SubjectFull: Retina Type: general – SubjectFull: Retina abnormalities Type: general – SubjectFull: Genetic algorithms Type: general Titles: – TitleFull: Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Quellec, Gwénolé – PersonEntity: Name: NameFull: Lamard, Mathieu – PersonEntity: Name: NameFull: Josselin, Pierre Marie – PersonEntity: Name: NameFull: Cazuguel, Guy – PersonEntity: Name: NameFull: Cochener, Beatrice – PersonEntity: Name: NameFull: Roux, Christian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 02780062 Numbering: – Type: volume Value: 27 – Type: issue Value: 9 Titles: – TitleFull: IEEE Transactions on Medical Imaging Type: main |
| ResultId | 1 |
