Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern
Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, sev...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Balodi, Arun [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 7 vom: 01. Jan., Seite 2205-2215 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:7 ; day:01 ; month:01 ; pages:2205-2215 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-018-3935-x |
|---|
| Katalog-ID: |
OLC2025618360 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2025618360 | ||
| 003 | DE-627 | ||
| 005 | 20230504125833.0 | ||
| 007 | tu | ||
| 008 | 200819s2019 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00521-018-3935-x |2 doi | |
| 035 | |a (DE-627)OLC2025618360 | ||
| 035 | |a (DE-He213)s00521-018-3935-x-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Balodi, Arun |e verfasserin |0 (orcid)0000-0002-6425-3299 |4 aut | |
| 245 | 1 | 0 | |a Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London Ltd., part of Springer Nature 2019 | ||
| 520 | |a Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. | ||
| 650 | 4 | |a Mitral regurgitation | |
| 650 | 4 | |a Texture analysis | |
| 650 | 4 | |a Gaussian pyramid | |
| 650 | 4 | |a Local binary patterns | |
| 650 | 4 | |a Computer-aided classification system | |
| 700 | 1 | |a Anand, R. S. |4 aut | |
| 700 | 1 | |a Dewal, M. L. |4 aut | |
| 700 | 1 | |a Rawat, Anurag |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural computing & applications |d Springer London, 1993 |g 32(2019), 7 vom: 01. Jan., Seite 2205-2215 |w (DE-627)165669608 |w (DE-600)1136944-9 |w (DE-576)032873050 |x 0941-0643 |7 nnns |
| 773 | 1 | 8 | |g volume:32 |g year:2019 |g number:7 |g day:01 |g month:01 |g pages:2205-2215 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00521-018-3935-x |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4277 | ||
| 951 | |a AR | ||
| 952 | |d 32 |j 2019 |e 7 |b 01 |c 01 |h 2205-2215 | ||
| author_variant |
a b ab r s a rs rsa m l d ml mld a r ar |
|---|---|
| matchkey_str |
article:09410643:2019----::optriecasfctooteirleugttouigutrsl |
| hierarchy_sort_str |
2019 |
| publishDate |
2019 |
| allfields |
10.1007/s00521-018-3935-x doi (DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p DE-627 ger DE-627 rakwb eng 004 VZ Balodi, Arun verfasserin (orcid)0000-0002-6425-3299 aut Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system Anand, R. S. aut Dewal, M. L. aut Rawat, Anurag aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 7 vom: 01. Jan., Seite 2205-2215 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 https://doi.org/10.1007/s00521-018-3935-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 7 01 01 2205-2215 |
| spelling |
10.1007/s00521-018-3935-x doi (DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p DE-627 ger DE-627 rakwb eng 004 VZ Balodi, Arun verfasserin (orcid)0000-0002-6425-3299 aut Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system Anand, R. S. aut Dewal, M. L. aut Rawat, Anurag aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 7 vom: 01. Jan., Seite 2205-2215 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 https://doi.org/10.1007/s00521-018-3935-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 7 01 01 2205-2215 |
| allfields_unstemmed |
10.1007/s00521-018-3935-x doi (DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p DE-627 ger DE-627 rakwb eng 004 VZ Balodi, Arun verfasserin (orcid)0000-0002-6425-3299 aut Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system Anand, R. S. aut Dewal, M. L. aut Rawat, Anurag aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 7 vom: 01. Jan., Seite 2205-2215 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 https://doi.org/10.1007/s00521-018-3935-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 7 01 01 2205-2215 |
| allfieldsGer |
10.1007/s00521-018-3935-x doi (DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p DE-627 ger DE-627 rakwb eng 004 VZ Balodi, Arun verfasserin (orcid)0000-0002-6425-3299 aut Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system Anand, R. S. aut Dewal, M. L. aut Rawat, Anurag aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 7 vom: 01. Jan., Seite 2205-2215 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 https://doi.org/10.1007/s00521-018-3935-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 7 01 01 2205-2215 |
| allfieldsSound |
10.1007/s00521-018-3935-x doi (DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p DE-627 ger DE-627 rakwb eng 004 VZ Balodi, Arun verfasserin (orcid)0000-0002-6425-3299 aut Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system Anand, R. S. aut Dewal, M. L. aut Rawat, Anurag aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 7 vom: 01. Jan., Seite 2205-2215 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 https://doi.org/10.1007/s00521-018-3935-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 7 01 01 2205-2215 |
| language |
English |
| source |
Enthalten in Neural computing & applications 32(2019), 7 vom: 01. Jan., Seite 2205-2215 volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 |
| sourceStr |
Enthalten in Neural computing & applications 32(2019), 7 vom: 01. Jan., Seite 2205-2215 volume:32 year:2019 number:7 day:01 month:01 pages:2205-2215 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Neural computing & applications |
| authorswithroles_txt_mv |
Balodi, Arun @@aut@@ Anand, R. S. @@aut@@ Dewal, M. L. @@aut@@ Rawat, Anurag @@aut@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
165669608 |
| dewey-sort |
14 |
| id |
OLC2025618360 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025618360</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504125833.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-018-3935-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025618360</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-018-3935-x-p</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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Balodi, Arun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-6425-3299</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mitral regurgitation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gaussian pyramid</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Local binary patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer-aided classification system</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Anand, R. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dewal, M. L.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rawat, Anurag</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">32(2019), 7 vom: 01. Jan., Seite 2205-2215</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:7</subfield><subfield code="g">day:01</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2205-2215</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-018-3935-x</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2019</subfield><subfield code="e">7</subfield><subfield code="b">01</subfield><subfield code="c">01</subfield><subfield code="h">2205-2215</subfield></datafield></record></collection>
|
| author |
Balodi, Arun |
| spellingShingle |
Balodi, Arun ddc 004 misc Mitral regurgitation misc Texture analysis misc Gaussian pyramid misc Local binary patterns misc Computer-aided classification system Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| authorStr |
Balodi, Arun |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)165669608 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0941-0643 |
| topic_title |
004 VZ Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern Mitral regurgitation Texture analysis Gaussian pyramid Local binary patterns Computer-aided classification system |
| topic |
ddc 004 misc Mitral regurgitation misc Texture analysis misc Gaussian pyramid misc Local binary patterns misc Computer-aided classification system |
| topic_unstemmed |
ddc 004 misc Mitral regurgitation misc Texture analysis misc Gaussian pyramid misc Local binary patterns misc Computer-aided classification system |
| topic_browse |
ddc 004 misc Mitral regurgitation misc Texture analysis misc Gaussian pyramid misc Local binary patterns misc Computer-aided classification system |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural computing & applications |
| hierarchy_parent_id |
165669608 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural computing & applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 |
| title |
Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| ctrlnum |
(DE-627)OLC2025618360 (DE-He213)s00521-018-3935-x-p |
| title_full |
Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| author_sort |
Balodi, Arun |
| journal |
Neural computing & applications |
| journalStr |
Neural computing & applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
2205 |
| author_browse |
Balodi, Arun Anand, R. S. Dewal, M. L. Rawat, Anurag |
| container_volume |
32 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Balodi, Arun |
| doi_str_mv |
10.1007/s00521-018-3935-x |
| normlink |
(ORCID)0000-0002-6425-3299 |
| normlink_prefix_str_mv |
(orcid)0000-0002-6425-3299 |
| dewey-full |
004 |
| title_sort |
computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| title_auth |
Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| abstract |
Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
7 |
| title_short |
Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern |
| url |
https://doi.org/10.1007/s00521-018-3935-x |
| remote_bool |
false |
| author2 |
Anand, R. S. Dewal, M. L. Rawat, Anurag |
| author2Str |
Anand, R. S. Dewal, M. L. Rawat, Anurag |
| ppnlink |
165669608 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-018-3935-x |
| up_date |
2024-07-04T01:43:31.923Z |
| _version_ |
1803610920777154561 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025618360</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504125833.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-018-3935-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025618360</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-018-3935-x-p</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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Balodi, Arun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-6425-3299</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mitral regurgitation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gaussian pyramid</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Local binary patterns</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer-aided classification system</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Anand, R. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dewal, M. L.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rawat, Anurag</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">32(2019), 7 vom: 01. Jan., Seite 2205-2215</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:7</subfield><subfield code="g">day:01</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2205-2215</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-018-3935-x</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2019</subfield><subfield code="e">7</subfield><subfield code="b">01</subfield><subfield code="c">01</subfield><subfield code="h">2205-2215</subfield></datafield></record></collection>
|
| score |
7.399811 |