InforLorV4, PascalFrancis, Corpus, bibRecord, 000116

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Identifieur interne : 000116 ( PascalFrancis/Corpus ); précédent : 000115; suivant : 000117

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Auteurs : F. Longuetaud ; F. Mothe ; B. Kerautret ; A. Kr Henbühl ; L. Hory ; J. M. Leban ; I. Debled-Rennesson

Source :

Computers and electronics in agriculture [ 0168-1699 ] ; 2012.

RBID : Pascal:12-0283258

Descripteurs français

Pascal (Inist)
- Automatique, Tomodensitométrie, Radiographie RX, Bois résineux, Article synthèse, Validation, Algorithme, Echantillon, Tomographie, Picea abies, Abies alba.

English descriptors

KwdEn :
- Abies alba, Algorithm, Automatic, Computerized axial tomography, Picea abies, Review, Sample, Softwood, Tomography, Validation, X ray radiography.

Abstract

An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R² of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A11	`07`	`1`		`@1 DEBLED-RENNESSON (I.)`
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A14	`03`			`@1 LORIA, UMR CNRS 7503, Université de Nancy, Campus Scientifique @2 54506 Vandœuvre-lès-Noncy @3 FRA @Z 3 aut. @Z 4 aut. @Z 5 aut. @Z 7 aut.`
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C02	`02`	`X`		`@0 002A33`
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C03	`01`	`X`	`ENG`	`@0 Automatic @5 01`
C03	`01`	`X`	`SPA`	`@0 Automático @5 01`
C03	`02`	`X`	`FRE`	`@0 Tomodensitométrie @5 02`
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C03	`04`	`X`	`FRE`	`@0 Bois résineux @5 04`
C03	`04`	`X`	`ENG`	`@0 Softwood @5 04`
C03	`04`	`X`	`SPA`	`@0 Madera de coníferas @5 04`
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C03	`07`	`X`	`SPA`	`@0 Algoritmo @5 07`
C03	`08`	`X`	`FRE`	`@0 Echantillon @5 08`
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C03	`10`	`X`	`FRE`	`@0 Picea abies @2 NS @5 10`
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C07	`01`	`X`	`FRE`	`@0 Coniferales @2 NS`
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C07	`03`	`X`	`FRE`	`@0 Spermatophyta @2 NS`
C07	`03`	`X`	`ENG`	`@0 Spermatophyta @2 NS`
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C07	`04`	`X`	`SPA`	`@0 Arbol forestal resinoso @5 31`
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N44	`01`			`@1 OTO`
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Format Inist (serveur)

NO :	PASCAL 12-0283258 INIST
ET :	Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples
AU :	LONGUETAUD (F.); MOTHE (F.); KERAUTRET (B.); KRÄHENBÜHL (A.); HORY (L.); LEBAN (J. M.); DEBLED-RENNESSON (I.)
AF :	INRA, UMR1092 LERFoB/54280 Champenoux/France (1 aut., 2 aut.); AgroParisTech, UMR1092 LERFoB/54000 Nancy/France (1 aut., 2 aut.); LORIA, UMR CNRS 7503, Université de Nancy, Campus Scientifique/54506 Vandœuvre-lès-Noncy/France (3 aut., 4 aut., 5 aut., 7 aut.); Université de Lorraine, ENSTIB, LERMaB/27 rue Philippe Seguin, Epinal/France (6 aut.)
DT :	Publication en série; Niveau analytique
SO :	Computers and electronics in agriculture; ISSN 0168-1699; Coden CEAGE6; Pays-Bas; Da. 2012; Vol. 85; Pp. 77-89; Bibl. 1 p.3/4
LA :	Anglais
EA :	An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R² of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.
CC :	002A32; 002A33
FD :	Automatique; Tomodensitométrie; Radiographie RX; Bois résineux; Article synthèse; Validation; Algorithme; Echantillon; Tomographie; Picea abies; Abies alba
FG :	Coniferales; Gymnospermae; Spermatophyta; Arbre forestier résineux
ED :	Automatic; Computerized axial tomography; X ray radiography; Softwood; Review; Validation; Algorithm; Sample; Tomography; Picea abies; Abies alba
EG :	Coniferales; Gymnospermae; Spermatophyta; Softwood forest tree
SD :	Automático; Tomodensitometría; Radiografía RX; Madera de coníferas; Artículo síntesis; Validación; Algoritmo; Muestra; Tomografía; Picea abies; Abies alba
LO :	INIST-21007.354000507998330110
ID :	12-0283258

Links to Exploration step

Pascal:12-0283258

Le document en format XML

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<front><div type="abstract" xml:lang="en">An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R<sup>2</sup>
 of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.</div>
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<ET>Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples</ET>
<AU>LONGUETAUD (F.); MOTHE (F.); KERAUTRET (B.); KRÄHENBÜHL (A.); HORY (L.); LEBAN (J. M.); DEBLED-RENNESSON (I.)</AU>
<AF>INRA, UMR1092 LERFoB/54280 Champenoux/France (1 aut., 2 aut.); AgroParisTech, UMR1092 LERFoB/54000 Nancy/France (1 aut., 2 aut.); LORIA, UMR CNRS 7503, Université de Nancy, Campus Scientifique/54506 Vandœuvre-lès-Noncy/France (3 aut., 4 aut., 5 aut., 7 aut.); Université de Lorraine, ENSTIB, LERMaB/27 rue Philippe Seguin, Epinal/France (6 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Computers and electronics in agriculture; ISSN 0168-1699; Coden CEAGE6; Pays-Bas; Da. 2012; Vol. 85; Pp. 77-89; Bibl. 1 p.3/4</SO>
<LA>Anglais</LA>
<EA>An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R<sup>2</sup>
 of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.</EA>
<CC>002A32; 002A33</CC>
<FD>Automatique; Tomodensitométrie; Radiographie RX; Bois résineux; Article synthèse; Validation; Algorithme; Echantillon; Tomographie; Picea abies; Abies alba</FD>
<FG>Coniferales; Gymnospermae; Spermatophyta; Arbre forestier résineux</FG>
<ED>Automatic; Computerized axial tomography; X ray radiography; Softwood; Review; Validation; Algorithm; Sample; Tomography; Picea abies; Abies alba</ED>
<EG>Coniferales; Gymnospermae; Spermatophyta; Softwood forest tree</EG>
<SD>Automático; Tomodensitometría; Radiografía RX; Madera de coníferas; Artículo síntesis; Validación; Algoritmo; Muestra; Tomografía; Picea abies; Abies alba</SD>
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Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

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