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Automatic detection and characterization of funnel chest based on spiral CT.

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Title: Automatic detection and characterization of funnel chest based on spiral CT.
Authors: Papp, Laszlo1 laszlo.papp@mediso.hu, Juhasz, Reka2, Travar, Sonja3, Kolli, Alexander4, Sorantin, Erich4
Source: Journal of X-Ray Science & Technology. 2010, Vol. 18 Issue 2, p137-144. 8p. 4 Black and White Photographs, 1 Diagram, 5 Charts, 2 Graphs.
Subjects: Chest abnormalities, Human abnormalities, Tomography, Curvature, Statistical correlation, Human factors in automation
Abstract: Funnel chest (Pectus excavatum) is the most common deformity of the anterior chest in children. Present paper describes a method to process and classify CT slices representing funnel chest deformities. A manually chosen CT slice was processed to detect the inner curvature of the chest for characterization. Normalized data from the detected inner curvature was gained and saved next to a manually-given deformity type for further classification rule determinations. Based on the multiple correlations of the values gained from the inner curvature, a hierarchical classification was performed on 199 patient data. Results have shown that the calculated values gained from the inner curvature can accurately characterize the deformity type of the chest. Since minimal user interaction was necessary to detect and characterize the inner curvature, our method is considered to be an effective automated procedure for funnel chest deformity classifications. [ABSTRACT FROM AUTHOR]
Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. 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
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  Data: Automatic detection and characterization of funnel chest based on spiral CT.
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  Data: <searchLink fieldCode="AR" term="%22Papp%2C+Laszlo%22">Papp, Laszlo</searchLink><relatesTo>1</relatesTo><i> laszlo.papp@mediso.hu</i><br /><searchLink fieldCode="AR" term="%22Juhasz%2C+Reka%22">Juhasz, Reka</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Travar%2C+Sonja%22">Travar, Sonja</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Kolli%2C+Alexander%22">Kolli, Alexander</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Sorantin%2C+Erich%22">Sorantin, Erich</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+X-Ray+Science+%26+Technology%22">Journal of X-Ray Science & Technology</searchLink>. 2010, Vol. 18 Issue 2, p137-144. 8p. 4 Black and White Photographs, 1 Diagram, 5 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Chest+abnormalities%22">Chest abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Human+abnormalities%22">Human abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Curvature%22">Curvature</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Human+factors+in+automation%22">Human factors in automation</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Funnel chest (Pectus excavatum) is the most common deformity of the anterior chest in children. Present paper describes a method to process and classify CT slices representing funnel chest deformities. A manually chosen CT slice was processed to detect the inner curvature of the chest for characterization. Normalized data from the detected inner curvature was gained and saved next to a manually-given deformity type for further classification rule determinations. Based on the multiple correlations of the values gained from the inner curvature, a hierarchical classification was performed on 199 patient data. Results have shown that the calculated values gained from the inner curvature can accurately characterize the deformity type of the chest. Since minimal user interaction was necessary to detect and characterize the inner curvature, our method is considered to be an effective automated procedure for funnel chest deformity classifications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of X-Ray Science & Technology is the property of Sage Publications Inc. 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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        Value: 10.3233/xst-2010-024900249
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        Text: English
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        Type: general
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      – SubjectFull: Tomography
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      – SubjectFull: Curvature
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      – SubjectFull: Statistical correlation
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      – SubjectFull: Human factors in automation
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