Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier
Highlights • A new CAD scheme for pulmonary nodule detection is proposed. • Dynamic self-adaptive template matching is used to detect nodules. • FLDA classifier can filter false positive detection nodules.
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| Veröffentlicht in: | Physica medica 2016-12, Vol.32 (12), p.1502-1509 |
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| container_end_page | 1509 |
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| container_issue | 12 |
| container_start_page | 1502 |
| container_title | Physica medica |
| container_volume | 32 |
| creator | Gong, Jing Liu, Ji-yu Wang, Li-jia Zheng, Bin Nie, Sheng-dong |
| description | Highlights • A new CAD scheme for pulmonary nodule detection is proposed. • Dynamic self-adaptive template matching is used to detect nodules. • FLDA classifier can filter false positive detection nodules. |
| doi_str_mv | 10.1016/j.ejmp.2016.11.001 |
| format | Article |
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| fulltext | fulltext |
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| ispartof | Physica medica, 2016-12, Vol.32 (12), p.1502-1509 |
| issn | 1120-1797 1724-191X |
| language | eng |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Computer-aided detection CT images Discriminant Analysis False Positive Reactions FLDA Humans Image Processing, Computer-Assisted Linear Models Lung Neoplasms - diagnostic imaging Pulmonary nodule Radiographic Image Interpretation, Computer-Assisted - methods Radiology Template matching Tomography, X-Ray Computed |
| title | Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier |
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