CT画像における結節状陰影検出の性能改良
We have developed an automated computerized schema for the detection of lung nodules in 3D CT images obtained by helical CT. In our previous schema, linear discriminant analysis (LDA) and a rule-based method with 53 image features were employed in order to reduce false positives. However, several fa...
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| Published in | 日本放射線技術学会雑誌 Vol. 64; no. 3; pp. 316 - 324 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
公益社団法人 日本放射線技術学会
2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0369-4305 1881-4883 |
| DOI | 10.6009/jjrt.64.316 |
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| Abstract | We have developed an automated computerized schema for the detection of lung nodules in 3D CT images obtained by helical CT. In our previous schema, linear discriminant analysis (LDA) and a rule-based method with 53 image features were employed in order to reduce false positives. However, several false positives have remained. Therefore, in this study, we improved the false-positive reduction technique by using the edge image and radial image analysis. Overall performance for the detection of lung nodules was greatly improved. Sensitivity was higher than that of our previous study. Moreover, we evaluated the overall performance of the new scheme by using 69 cases acquired from four hospitals. The average number of false positives was 5.2 per case at a percent sensitivity of 95.8%. Our new scheme would assist in the detection of early lung cancer. |
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| AbstractList | We have developed an automated computerized schema for the detection of lung nodules in 3D CT images obtained by helical CT. In our previous schema, linear discriminant analysis (LDA) and a rule-based method with 53 image features were employed in order to reduce false positives. However, several false positives have remained. Therefore, in this study, we improved the false-positive reduction technique by using the edge image and radial image analysis. Overall performance for the detection of lung nodules was greatly improved. Sensitivity was higher than that of our previous study. Moreover, we evaluated the overall performance of the new scheme by using 69 cases acquired from four hospitals. The average number of false positives was 5.2 per case at a percent sensitivity of 95.8%. Our new scheme would assist in the detection of early lung cancer. |
| Author | 影本, 正行 祖母井, 努 石田, 隆行 水戸川, 芳巳 伊藤, 勝陽 秋山, 實利 藤川, 光一 川下, 郁生 石根, 正博 山本, めぐみ 大倉, 保彦 |
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| References | 3）重本加奈恵，滝沢穂高，山本眞司，他：3 次元結節・血管モデルとテンプレートマッチングを用いた胸部X線CT画像からの結節陰影の高速認識．Med Imag Tech，21（2），147-155，（2003 10) Jiang H, Yamamoto S, Iisaku S, et al.: Computer-aided diagnosis system for lung cancer screening by CT. in Doi K, et al. (eds) : Computer-Aided Diagnosis in Medical Imaging. pp.25-30, (1999). 2）上巻第55表病院数（重複計上）台数，診療機器・一般病院（再掲）・開設者別，厚生労働省データベース，医療施設調査，（1999 7) Arimura H, Katsuragawa S, Suzuki K, et al.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol, 11 (6), 617-629, (2004). 14) Gurcan MN, Sahiner B, Petrick N, et al.: Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys, 29 (11), 2552-2558. (2002). 4）李 鎔範，原 武史，藤田広志：胸部ヘリカルCT画像を用いたシミュレーションによるGAテンプレートマッチング法の評価．医用画像情報通信学会雑誌，17（3），118-129，（2000 6) Li Q, Sone S, Doi K: Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 30 (8), 2040-2051, (2003). 1）上巻第87表病院数（重複計上）台数・取り扱延件数・診療機器・一般病院（再掲）・開設者別，厚生労働省データベース，医療施設調査，（1999 8) Giger ML, Doi K, MacMahon H: Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys, 15 (2), 158-166, (1988). 17) Lachenbruch PA: Discriminant Analysis. New York: Hafner, Chaps. 1 and 2, pp.1-32, Macmillan, (1975). 18) Li Q, Doi K: Analysis and minimization of overtraining effect in rule-base classifiers for computer-aided diagnosis, Med Phys, 33 (2), 320-328, (2006). 16）山本めぐみ，石田隆行，川下郁生，他：胸部三次元CT画像における結節状陰影の自動検出法の開発．日放技学誌，62（4），555-564，（2006 9) Yamamoto S, Tanaka I, Senda M, et al.: Image processing for computer-aided diagnosis of lung cancer by CT (LSCT). Systems and Computers in Japan, 25 (2), 67-80, (1994). 5) Lee Y, Hara T, Fujita H, et al.: Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging, 20 (7), 595-604, (2001). 15) Brown MS, Goldin JG, Suh RD, et al.: Lung micronodules: automated method for detection at thin-section CT-initial experience. Radiology, 226 (1), 256-262, (2003). 11) Ukai Y, Niki N, Satoh H, et al.: Computer aided diagnosis system for lung cancer based on retrospective helical CT image. Proc. SPIE, 3979, 1028-1039, (2000). 13) Wormanns D, Fiebich M, Saidi M, et al.: Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system. Eur Radiol, 12 (5), 1052-1057, (2002). 12) Armato SG 3rd, Li F, Giger ML, et al.: Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology, 225 (3), 685-692, (2002). |
| References_xml | – reference: 5) Lee Y, Hara T, Fujita H, et al.: Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging, 20 (7), 595-604, (2001). – reference: 9) Yamamoto S, Tanaka I, Senda M, et al.: Image processing for computer-aided diagnosis of lung cancer by CT (LSCT). Systems and Computers in Japan, 25 (2), 67-80, (1994). – reference: 2）上巻第55表病院数（重複計上）台数，診療機器・一般病院（再掲）・開設者別，厚生労働省データベース，医療施設調査，（1999）． – reference: 3）重本加奈恵，滝沢穂高，山本眞司，他：3 次元結節・血管モデルとテンプレートマッチングを用いた胸部X線CT画像からの結節陰影の高速認識．Med Imag Tech，21（2），147-155，（2003）． – reference: 6) Li Q, Sone S, Doi K: Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 30 (8), 2040-2051, (2003). – reference: 18) Li Q, Doi K: Analysis and minimization of overtraining effect in rule-base classifiers for computer-aided diagnosis, Med Phys, 33 (2), 320-328, (2006). – reference: 11) Ukai Y, Niki N, Satoh H, et al.: Computer aided diagnosis system for lung cancer based on retrospective helical CT image. Proc. SPIE, 3979, 1028-1039, (2000). – reference: 12) Armato SG 3rd, Li F, Giger ML, et al.: Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology, 225 (3), 685-692, (2002). – reference: 17) Lachenbruch PA: Discriminant Analysis. New York: Hafner, Chaps. 1 and 2, pp.1-32, Macmillan, (1975). – reference: 14) Gurcan MN, Sahiner B, Petrick N, et al.: Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys, 29 (11), 2552-2558. (2002). – reference: 4）李 鎔範，原 武史，藤田広志：胸部ヘリカルCT画像を用いたシミュレーションによるGAテンプレートマッチング法の評価．医用画像情報通信学会雑誌，17（3），118-129，（2000）． – reference: 13) Wormanns D, Fiebich M, Saidi M, et al.: Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system. Eur Radiol, 12 (5), 1052-1057, (2002). – reference: 7) Arimura H, Katsuragawa S, Suzuki K, et al.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol, 11 (6), 617-629, (2004). – reference: 15) Brown MS, Goldin JG, Suh RD, et al.: Lung micronodules: automated method for detection at thin-section CT-initial experience. Radiology, 226 (1), 256-262, (2003). – reference: 10) Jiang H, Yamamoto S, Iisaku S, et al.: Computer-aided diagnosis system for lung cancer screening by CT. in Doi K, et al. (eds) : Computer-Aided Diagnosis in Medical Imaging. pp.25-30, (1999). – reference: 1）上巻第87表病院数（重複計上）台数・取り扱延件数・診療機器・一般病院（再掲）・開設者別，厚生労働省データベース，医療施設調査，（1999）． – reference: 8) Giger ML, Doi K, MacMahon H: Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys, 15 (2), 158-166, (1988). – reference: 16）山本めぐみ，石田隆行，川下郁生，他：胸部三次元CT画像における結節状陰影の自動検出法の開発．日放技学誌，62（4），555-564，（2006）． |
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| Snippet | We have developed an automated computerized schema for the detection of lung nodules in 3D CT images obtained by helical CT. In our previous schema, linear... |
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| SubjectTerms | computed tomography (CT) computer-aided diagnosis (CAD) cross-correlation lung cancer lung nodule |
| Title | CT画像における結節状陰影検出の性能改良 |
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