Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images
Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodul...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Silva, Aristófanes C. [verfasserIn] Carvalho, Paulo Cezar P. [verfasserIn] Gattass, Marcelo [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2004 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 7(2004), 3 vom: 30. Juli, Seite 227-234 |
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| Übergeordnetes Werk: |
volume:7 ; year:2004 ; number:3 ; day:30 ; month:07 ; pages:227-234 |
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| DOI / URN: |
10.1007/s10044-004-0219-0 |
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| 520 | |a Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. | ||
| 650 | 4 | |a Diagnosis of lung nodule |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Gattass, Marcelo |e verfasserin |4 aut | |
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10.1007/s10044-004-0219-0 doi (DE-627)SPR008210144 (SPR)s10044-004-0219-0-e DE-627 ger DE-627 rakwb eng Silva, Aristófanes C. verfasserin aut Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 Carvalho, Paulo Cezar P. verfasserin aut Gattass, Marcelo verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 7(2004), 3 vom: 30. Juli, Seite 227-234 (DE-627)SPR008209189 nnns volume:7 year:2004 number:3 day:30 month:07 pages:227-234 https://dx.doi.org/10.1007/s10044-004-0219-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2004 3 30 07 227-234 |
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10.1007/s10044-004-0219-0 doi (DE-627)SPR008210144 (SPR)s10044-004-0219-0-e DE-627 ger DE-627 rakwb eng Silva, Aristófanes C. verfasserin aut Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 Carvalho, Paulo Cezar P. verfasserin aut Gattass, Marcelo verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 7(2004), 3 vom: 30. Juli, Seite 227-234 (DE-627)SPR008209189 nnns volume:7 year:2004 number:3 day:30 month:07 pages:227-234 https://dx.doi.org/10.1007/s10044-004-0219-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2004 3 30 07 227-234 |
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10.1007/s10044-004-0219-0 doi (DE-627)SPR008210144 (SPR)s10044-004-0219-0-e DE-627 ger DE-627 rakwb eng Silva, Aristófanes C. verfasserin aut Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 Carvalho, Paulo Cezar P. verfasserin aut Gattass, Marcelo verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 7(2004), 3 vom: 30. Juli, Seite 227-234 (DE-627)SPR008209189 nnns volume:7 year:2004 number:3 day:30 month:07 pages:227-234 https://dx.doi.org/10.1007/s10044-004-0219-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2004 3 30 07 227-234 |
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10.1007/s10044-004-0219-0 doi (DE-627)SPR008210144 (SPR)s10044-004-0219-0-e DE-627 ger DE-627 rakwb eng Silva, Aristófanes C. verfasserin aut Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 Carvalho, Paulo Cezar P. verfasserin aut Gattass, Marcelo verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 7(2004), 3 vom: 30. Juli, Seite 227-234 (DE-627)SPR008209189 nnns volume:7 year:2004 number:3 day:30 month:07 pages:227-234 https://dx.doi.org/10.1007/s10044-004-0219-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2004 3 30 07 227-234 |
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10.1007/s10044-004-0219-0 doi (DE-627)SPR008210144 (SPR)s10044-004-0219-0-e DE-627 ger DE-627 rakwb eng Silva, Aristófanes C. verfasserin aut Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 Carvalho, Paulo Cezar P. verfasserin aut Gattass, Marcelo verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 7(2004), 3 vom: 30. Juli, Seite 227-234 (DE-627)SPR008209189 nnns volume:7 year:2004 number:3 day:30 month:07 pages:227-234 https://dx.doi.org/10.1007/s10044-004-0219-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 7 2004 3 30 07 227-234 |
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Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images Diagnosis of lung nodule (dpeaa)DE-He213 Semivariogram (dpeaa)DE-He213 Semimadogram (dpeaa)DE-He213 Covariogram (dpeaa)DE-He213 Correlogram (dpeaa)DE-He213 |
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Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images |
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Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. |
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Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. |
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Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008210144</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023755.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2004 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10044-004-0219-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008210144</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10044-004-0219-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Silva, Aristófanes C.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2004</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper analyzes four geostatistical functions—semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnosis of lung nodule</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semivariogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semimadogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Covariogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Correlogram</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Carvalho, Paulo Cezar P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gattass, Marcelo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern Analysis & Applications</subfield><subfield code="d">Springer-Verlag, 1999</subfield><subfield code="g">7(2004), 3 vom: 30. Juli, Seite 227-234</subfield><subfield code="w">(DE-627)SPR008209189</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:3</subfield><subfield code="g">day:30</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:227-234</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10044-004-0219-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2004</subfield><subfield code="e">3</subfield><subfield code="b">30</subfield><subfield code="c">07</subfield><subfield code="h">227-234</subfield></datafield></record></collection>
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