Fusion of local and global detection systems to detect tuberculosis in chest radiographs

L.E. Hogeweg; C. Mol; P.A. de Jong; R. Dawson; H. Ayles; B. van Ginneken

doi:10.1007/978-3-642-15711-0_81

Fusion of local and global detection systems to detect tuberculosis in chest radiographs

L.E. Hogeweg, C. Mol, P.A. de Jong, R. Dawson, H. Ayles, B. van Ginneken

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

15 Citations (Scopus)

Abstract

Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Editors	T. Jiang, N. Navab, J.P.W. Pluim, M.A.. Viergever
Publisher	Springer
Pages	650-657
Number of pages	8
Edition	PART 3
ISBN (Print)	3642157106, 9783642157103
DOIs	https://doi.org/10.1007/978-3-642-15711-0_81
Publication status	Published - 22 Nov 2010
Event	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: 20 Sept 2010 → 24 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	6363 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 24/09/10

Keywords

chest radiography
computer aided detection
system combination
tuberculosis

Access to Document

10.1007/978-3-642-15711-0_81

http://www.ncbi.nlm.nih.gov/pubmed/20879456

Cite this

Hogeweg, L. E., Mol, C., de Jong, P. A., Dawson, R., Ayles, H., & van Ginneken, B. (2010). Fusion of local and global detection systems to detect tuberculosis in chest radiographs. In T. Jiang, N. Navab, J. P. W. Pluim, & M. A. Viergever (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 3 ed., pp. 650-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3). Springer. https://doi.org/10.1007/978-3-642-15711-0_81

Hogeweg, L.E. ; Mol, C. ; de Jong, P.A. et al. / Fusion of local and global detection systems to detect tuberculosis in chest radiographs. Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. editor / T. Jiang ; N. Navab ; J.P.W. Pluim ; M.A.. Viergever. PART 3. ed. Springer, 2010. pp. 650-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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abstract = "Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.",

keywords = "chest radiography, computer aided detection, system combination, tuberculosis",

author = "L.E. Hogeweg and C. Mol and {de Jong}, P.A. and R. Dawson and H. Ayles and {van Ginneken}, B.",

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Hogeweg, LE, Mol, C, de Jong, PA, Dawson, R, Ayles, H & van Ginneken, B 2010, Fusion of local and global detection systems to detect tuberculosis in chest radiographs. in T Jiang, N Navab, JPW Pluim & MA Viergever (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 6363 LNCS, Springer, pp. 650-657, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15711-0_81

Fusion of local and global detection systems to detect tuberculosis in chest radiographs. / Hogeweg, L.E.; Mol, C.; de Jong, P.A. et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. ed. / T. Jiang; N. Navab; J.P.W. Pluim; M.A.. Viergever. PART 3. ed. Springer, 2010. p. 650-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6363 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Mol, C.

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AU - Dawson, R.

AU - Ayles, H.

AU - van Ginneken, B.

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N2 - Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

AB - Automatic detection of tuberculosis (TB) on chest radiographs is a difficult problem because of the diverse presentation of the disease. A combination of detection systems for abnormalities and normal anatomy is used to improve detection performance. A textural abnormality detection system operating at the pixel level is combined with a clavicle detection system to suppress false positive responses. The output of a shape abnormality detection system operating at the image level is combined in a next step to further improve performance by reducing false negatives. Strategies for combining systems based on serial and parallel configurations were evaluated using the minimum, maximum, product, and mean probability combination rules. The performance of TB detection increased, as measured using the area under the ROC curve, from 0.67 for the textural abnormality detection system alone to 0.86 when the three systems were combined. The best result was achieved using the sum and product rule in a parallel combination of outputs.

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A2 - Navab, N.

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Hogeweg LE, Mol C, de Jong PA, Dawson R, Ayles H, van Ginneken B. Fusion of local and global detection systems to detect tuberculosis in chest radiographs. In Jiang T, Navab N, Pluim JPW, Viergever MA, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 3 ed. Springer. 2010. p. 650-657. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-15711-0_81

Fusion of local and global detection systems to detect tuberculosis in chest radiographs

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