Fig 1.
Diagram of the lung nodule image sequence segmentation method.
Fig 2.
Mapping relationship between CT sequence images and AIP sequence images.
Table 1.
HMSLIC algorithm.
Table 2.
DBSCAN algorithm.
Fig 3.
The sum of the distance between superpixel blocks.
Fig 4.
The relationship of the distance between index_max and other superpixels.
Table 3.
Determine the threshold algorithm.
Table 4.
Improved DBSCAN algorithm.
Table 5.
Lung nodule image sequence segmentation algorithm.
Table 6.
The parameter settings of our method.
Fig 5.
The sequence segmentation results of our method for solitary pulmonary nodules.
Column (a) shows the original lung CT sequence images, (b) shows the results of the local enlargement of (a), (c) shows the segmentation results of HMSLIC (first image) and the extraction of ROI images (other images), (d) shows the local enlargement of (c) (first image) and the segmentation results of the ROI images using HMSLIC (other images), (e) shows lung nodule image mask sequences obtained by the Improved DBSCAN algorithm, (f) and (g) present the final results using our method and manual segmentation by experts.
Fig 6.
The sequence segmentation results of our method for cavitary nodules.
The detailed descriptions are the same as in Fig 5.
Fig 7.
The sequence segmentation results of our method for juxta-vascular nodules.
The detailed descriptions are the same as in Fig 5.
Fig 8.
The segmentation results of our method for solitary pulmonary nodules and cavitary nodules.
Column (a) shows the original lung CT images, (b) shows the results of the local enlargement of (a), (c) shows the segmentation results of HMSLIC, (d) shows the results of the local enlargement of (c), (e) shows the lung nodule image masks obtained by the Improved DBSCAN algorithm, and (f) and (g) present the final results using our method and manual segmentation by experts.
Fig 9.
The segmentation results of our method for juxta-vascular nodules.
The detailed descriptions are the same as shown in Fig 8.
Fig 10.
The segmentation results of RG for solitary pulmonary nodules and cavitary nodules.
Column (a) shows the original lung CT images, (b) shows the results of the local enlargement of (a), (c)-(f) show the segmentation results of lung nodule image masks when the gray threshold is 0.05, 0.1, 0.15 and 0.2 (/255), and (g) shows the results of manual segmentation by experts.
Fig 11.
The segmentation results of RG for juxta-vascular nodules.
The detailed descriptions are the same as presented in Fig 10.
Fig 12.
Comparison of the segmentation results of seven methods for solitary pulmonary nodules and cavitary nodules.
Column (a) shows the original lung CT images, (b)-(h) show the results of the lung nodule image masks using RG (gray threshold is 0.2), PCNN, KM, FCM, PSO-SGNN, FEGD and our method, and (i) shows the results of manual segmentation by experts.
Fig 13.
Comparison of the segmentation results of seven methods for juxta-vascular nodules.
Column (b) shows the results of the lung nodule image masks using RG (gray threshold of 0.15). The other detailed descriptions are the same as in Fig 12.
Fig 14.
3D reconstruction of a cavitary nodule.
Fig 15.
3D reconstruction of juxta-vascular nodules and surrounding non-target tissues.
Fig 16.
The PRI curves of the segmentation results of three types of nodule images.
Fig 17.
The GCE curves of the segmentation results for three type of nodule images.
Fig 18.
The VoI curves of the segmentation results of three types of nodule images.
Table 7.
The mean PRI, GCE and VoI for the five algorithms in all experimental datasets.
Table 8.
Average execution time (s) of the five algorithms for all CT image sequences.