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Pulmonary Nodules: Automated Detection on CT Images with Morphologic Matching Algorithm—Preliminary Results¹

¹ From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110. Received July 23, 2004; revision requested September 28; revision received October 23; accepted December 10. Address correspondence to K.T.B. (e-mail: baet{at}mir.wustl.edu).

View larger version (26K): [in a new window]   Figure 1. Bar graph shows the number and size distribution of lung nodules in 20 patients. A total of 164 nodules was identified. Eighteen of 20 patients had 1–13 pulmonary nodules (mean, 4.7 nodules), and the other two patients had 25 and 54 nodules on CT images.

View larger version (36K): [in a new window]   Figure 2. Flow diagram illustrates the overall scheme for automated lung nodule detection on CT images.

View larger version (149K): [in a new window]   Figure 3a. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (52K): [in a new window]   Figure 3b. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (42K): [in a new window]   Figure 3c. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (32K): [in a new window]   Figure 3d. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (110K): [in a new window]   Figure 3e. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (44K): [in a new window]   Figure 3f. Patient 5. Intermediate 2D CT images illustrate the lung region segmentation process. (a) Transverse CT image of the thorax underwent gray-level thresholding to generate (b) a binary image. (c) An initial uncorrected lung region was estimated. (d) After a corrected lung boundary was determined, (e) the initial CT image enclosed by this boundary was isolated. (f) The soft-tissue structures within the lung region were segmented by means of gray-level thresholding.

View larger version (122K): [in a new window]   Figure 4a. Patient 15. Segmented 3D volumetric data (a) before and (b) after removing the nonvessel group. Image a was obtained by applying region growing and labeling to a stack of 2D segmented lung images (one of which is shown in Fig 3f). Image a contains three types of lung nodules (isolated, juxtapleural, and juxtavascular), blood vessels, and noise voxels. The 3D data set in b—that is, the vessel group, including juxtavascular nodules—represents a subset of the 3D data in a, after the structures not connected to the pulmonary vessels were removed.

View larger version (95K): [in a new window]   Figure 4b. Patient 15. Segmented 3D volumetric data (a) before and (b) after removing the nonvessel group. Image a was obtained by applying region growing and labeling to a stack of 2D segmented lung images (one of which is shown in Fig 3f). Image a contains three types of lung nodules (isolated, juxtapleural, and juxtavascular), blood vessels, and noise voxels. The 3D data set in b—that is, the vessel group, including juxtavascular nodules—represents a subset of the 3D data in a, after the structures not connected to the pulmonary vessels were removed.

View larger version (146K): [in a new window]   Figure 5a. Patient 5. (a, c) Transverse CT images (b, d) with nodules detected by the CAD system. Image b shows juxtavascular (arrow) and isolated (arrowhead) nodules detected on the CT image in a. Image d shows two juxtapleural nodules (arrows) detected on the CT image in c. No false-negative or false-positive findings are observed on these two CT images.

View larger version (144K): [in a new window]   Figure 5b. Patient 5. (a, c) Transverse CT images (b, d) with nodules detected by the CAD system. Image b shows juxtavascular (arrow) and isolated (arrowhead) nodules detected on the CT image in a. Image d shows two juxtapleural nodules (arrows) detected on the CT image in c. No false-negative or false-positive findings are observed on these two CT images.

View larger version (149K): [in a new window]   Figure 5c. Patient 5. (a, c) Transverse CT images (b, d) with nodules detected by the CAD system. Image b shows juxtavascular (arrow) and isolated (arrowhead) nodules detected on the CT image in a. Image d shows two juxtapleural nodules (arrows) detected on the CT image in c. No false-negative or false-positive findings are observed on these two CT images.

View larger version (145K): [in a new window]   Figure 5d. Patient 5. (a, c) Transverse CT images (b, d) with nodules detected by the CAD system. Image b shows juxtavascular (arrow) and isolated (arrowhead) nodules detected on the CT image in a. Image d shows two juxtapleural nodules (arrows) detected on the CT image in c. No false-negative or false-positive findings are observed on these two CT images.

View larger version (113K): [in a new window]   Figure 6a. Patient 15. (a) Anterior and (b) posterior views of a 3D volumetric representation of pulmonary vessels and detected lung nodules (arrows). This patient had a total of 13 nodules (seven isolated, three juxtapleural, and three juxtavascular nodules), all of which were detected with the CAD system. There were two false-positive findings in this case.

View larger version (124K): [in a new window]   Figure 6b. Patient 15. (a) Anterior and (b) posterior views of a 3D volumetric representation of pulmonary vessels and detected lung nodules (arrows). This patient had a total of 13 nodules (seven isolated, three juxtapleural, and three juxtavascular nodules), all of which were detected with the CAD system. There were two false-positive findings in this case.

View larger version (33K): [in a new window]   Figure 7. Bar graph shows the number of lung nodules detected by the CAD system and a chest radiologist (first reading without CAD and second reading with CAD) in 20 patients. Lung nodule detection by the CAD system was highly accurate and better than the radiologist's first reading without CAD.