Abstract
Purpose: Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations. Methods: Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters. Results: The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4-36 times faster than existing methods. Conclusion: Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.
| Original language | English |
|---|---|
| Pages (from-to) | 193-205 |
| Number of pages | 13 |
| Journal | International journal of computer assisted radiology and surgery |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 01-01-2013 |
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All Science Journal Classification (ASJC) codes
- Surgery
- Radiology Nuclear Medicine and imaging
- Health Informatics
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Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. / Teramoto, Atsushi; Fujita, Hiroshi.
In: International journal of computer assisted radiology and surgery, Vol. 8, No. 2, 01.01.2013, p. 193-205.Research output: Contribution to journal › Article
TY - JOUR
T1 - Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter
AU - Teramoto, Atsushi
AU - Fujita, Hiroshi
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Purpose: Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations. Methods: Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters. Results: The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4-36 times faster than existing methods. Conclusion: Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.
AB - Purpose: Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations. Methods: Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters. Results: The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4-36 times faster than existing methods. Conclusion: Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.
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UR - http://www.scopus.com/inward/citedby.url?scp=84878889029&partnerID=8YFLogxK
U2 - 10.1007/s11548-012-0767-5
DO - 10.1007/s11548-012-0767-5
M3 - Article
C2 - 22684487
AN - SCOPUS:84878889029
VL - 8
SP - 193
EP - 205
JO - Computer-Assisted Radiology and Surgery
JF - Computer-Assisted Radiology and Surgery
SN - 1861-6410
IS - 2
ER -