Two-dimensional extrapolation methods for texture analysis on CT scans.
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| Title: | Two-dimensional extrapolation methods for texture analysis on CT scans. |
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| Authors: | Sensakovic, William F.1, Starkey, Adam1, Armato, Samuel G.1 |
| Source: | Medical Physics. Sep2007, Vol. 34 Issue 9, p3465-3472. 8p. 4 Black and White Photographs, 2 Charts, 2 Graphs. |
| Subjects: | Tomography, Diagnostic imaging, Scanning systems, Extrapolation, Medical imaging systems |
| Abstract: | The application of texture analysis to medical images may require the calculation of texture descriptors on regions of interest (ROIs) that are not completely filled by the tissue under analysis. If a texture descriptor is calculated using such “deficient” ROIs, the accuracy and computational speed may be adversely affected. This study applied 198 texture descriptors from five texture classes (first-order statistical, second-order statistical, Fourier, fractal, and Laws’ filtered) to lung parenchyma ROIs automatically extracted from the thoracic CT scans of ten patients. Statistically significant differences in the values of 138 of these texture descriptors were demonstrated when calculated on deficient ROIs. Three extrapolation methods (mean fill, tiled fill, and CLEAN deconvolution) then were applied to correct the deficient ROIs. Texture descriptor values were calculated and compared for the original, deficient, and corrected ROIs (based on the three extrapolation methods). Each extrapolation method induced statistically significant improvements in texture descriptor accuracy for some subset of texture descriptors. CLEAN deconvolution improved the greatest number of descriptors, demonstrated the best overall accuracy, and created ROIs that were visually most similar to the original ROIs. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 26445416 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Two-dimensional extrapolation methods for texture analysis on CT scans. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sensakovic%2C+William+F%2E%22">Sensakovic, William F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Starkey%2C+Adam%22">Starkey, Adam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Armato%2C+Samuel+G%2E%22">Armato, Samuel G.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Sep2007, Vol. 34 Issue 9, p3465-3472. 8p. 4 Black and White Photographs, 2 Charts, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Scanning+systems%22">Scanning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The application of texture analysis to medical images may require the calculation of texture descriptors on regions of interest (ROIs) that are not completely filled by the tissue under analysis. If a texture descriptor is calculated using such “deficient” ROIs, the accuracy and computational speed may be adversely affected. This study applied 198 texture descriptors from five texture classes (first-order statistical, second-order statistical, Fourier, fractal, and Laws’ filtered) to lung parenchyma ROIs automatically extracted from the thoracic CT scans of ten patients. Statistically significant differences in the values of 138 of these texture descriptors were demonstrated when calculated on deficient ROIs. Three extrapolation methods (mean fill, tiled fill, and CLEAN deconvolution) then were applied to correct the deficient ROIs. Texture descriptor values were calculated and compared for the original, deficient, and corrected ROIs (based on the three extrapolation methods). Each extrapolation method induced statistically significant improvements in texture descriptor accuracy for some subset of texture descriptors. CLEAN deconvolution improved the greatest number of descriptors, demonstrated the best overall accuracy, and created ROIs that were visually most similar to the original ROIs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell 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: 10.1118/1.2760307 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 3465 Subjects: – SubjectFull: Tomography Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Scanning systems Type: general – SubjectFull: Extrapolation Type: general – SubjectFull: Medical imaging systems Type: general Titles: – TitleFull: Two-dimensional extrapolation methods for texture analysis on CT scans. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sensakovic, William F. – PersonEntity: Name: NameFull: Starkey, Adam – PersonEntity: Name: NameFull: Armato, Samuel G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 34 – Type: issue Value: 9 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
