Hessian-MRLoG : Hessian information and multi-scale reverse LoG filter for pulmonary nodule detection
Copyright © 2021 Elsevier Ltd. All rights reserved..
Computer-aided detection (CADe) of pulmonary nodules is an effective approach for early detection of lung cancer. However, due to the low contrast of lung computed tomography (CT) images, the interference of blood vessels and classifications, CADe has the problems of low detection rate and high false-positive rate (FPR). To solve these problems, a novel method using Hessian information and multi-scale reverse Laplacian of Gaussian (LoG) (Hessian-MRLoG) is proposed and developed in this work. Also, since the intensity distribution of the LoG operator and the lung nodule in CT images are inconsistent, and their shapes are mismatched, a multi-scale reverse Laplacian of Gaussian (MRLoG) is constructed. In addition, in order to enhance the effectiveness of target detection, the second-order partial derivatives of MRLoG are partially adjusted by introducing an adjustment factor. On this basis, the Hessian-MRLoG model is developed, and a novel elliptic filter is designed. Ultimately, in this study, the method of Hessian-MRLoG filtering is proposed and developed for pulmonary nodule detection. To verify its effectiveness and accuracy, the proposed method was used to analyze the LUNA16 dataset. The experimental results revealed that the proposed method had an accuracy of 93.6% and produced 1.0 false positives per scan (FPs/scan), indicating that the proposed method can improve the detection rate and significantly reduce the FPR. Therefore, the proposed method has the potential for application in the detection, localization and labeling of other lesion areas.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:131 |
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| Enthalten in: |
Computers in biology and medicine - 131(2021) vom: 01. Apr., Seite 104272 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mao, Qi [VerfasserIn] |
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| Links: |
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| Themen: |
Computer-aided detection (CADe) |
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| Anmerkungen: |
Date Completed 02.07.2021 Date Revised 02.07.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiomed.2021.104272 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321945514 |
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| 500 | |a Date Revised 02.07.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Computer-aided detection (CADe) of pulmonary nodules is an effective approach for early detection of lung cancer. However, due to the low contrast of lung computed tomography (CT) images, the interference of blood vessels and classifications, CADe has the problems of low detection rate and high false-positive rate (FPR). To solve these problems, a novel method using Hessian information and multi-scale reverse Laplacian of Gaussian (LoG) (Hessian-MRLoG) is proposed and developed in this work. Also, since the intensity distribution of the LoG operator and the lung nodule in CT images are inconsistent, and their shapes are mismatched, a multi-scale reverse Laplacian of Gaussian (MRLoG) is constructed. In addition, in order to enhance the effectiveness of target detection, the second-order partial derivatives of MRLoG are partially adjusted by introducing an adjustment factor. On this basis, the Hessian-MRLoG model is developed, and a novel elliptic filter is designed. Ultimately, in this study, the method of Hessian-MRLoG filtering is proposed and developed for pulmonary nodule detection. To verify its effectiveness and accuracy, the proposed method was used to analyze the LUNA16 dataset. The experimental results revealed that the proposed method had an accuracy of 93.6% and produced 1.0 false positives per scan (FPs/scan), indicating that the proposed method can improve the detection rate and significantly reduce the FPR. Therefore, the proposed method has the potential for application in the detection, localization and labeling of other lesion areas | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Computer-aided detection (CADe) | |
| 650 | 4 | |a False-positive rate | |
| 650 | 4 | |a Filter | |
| 650 | 4 | |a Hessian information | |
| 650 | 4 | |a Pulmonary nodule | |
| 700 | 1 | |a Zhao, Shuguang |e verfasserin |4 aut | |
| 700 | 1 | |a Tong, Dongbing |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Shengchao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Xiang |e verfasserin |4 aut | |
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