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A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm.

Zhao J, Ji G, Qiang Y, Han X, Pei B, Shi Z - PLoS ONE (2015)

Bottom Line: Then, an improved watershed method was used to mark suspicious areas on the CT image.Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.

ABSTRACT

Background: Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.

Method: Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.

Results: Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

No MeSH data available.


Related in: MedlinePlus

Segmentation of improved watershed algorithm.(a) original SPN image; (b) segmented SPN in the lung; (c) marked points on the SPN; (d), (e), (f), and (g) penetrated areas.
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pone.0123694.g002: Segmentation of improved watershed algorithm.(a) original SPN image; (b) segmented SPN in the lung; (c) marked points on the SPN; (d), (e), (f), and (g) penetrated areas.

Mentions: To split the full ROI, this method requires a plurality of seed points in the target area. Eventually, the dam floods the internal dams, leaving only the outermost dams. The final boundaries of the ROI are then determined. Fig 2 shows the segmentation process of the improved watershed algorithm. If the heights of the penetrated areas are less than those of the adjacent water holes, then the water holes begin to leak. To prevent leakage from the water hole, dams are built on the boundaries of the areas. Finally, all the dams connect together and form the boundary of the segmentation.


A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm.

Zhao J, Ji G, Qiang Y, Han X, Pei B, Shi Z - PLoS ONE (2015)

Segmentation of improved watershed algorithm.(a) original SPN image; (b) segmented SPN in the lung; (c) marked points on the SPN; (d), (e), (f), and (g) penetrated areas.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4390287&req=5

pone.0123694.g002: Segmentation of improved watershed algorithm.(a) original SPN image; (b) segmented SPN in the lung; (c) marked points on the SPN; (d), (e), (f), and (g) penetrated areas.
Mentions: To split the full ROI, this method requires a plurality of seed points in the target area. Eventually, the dam floods the internal dams, leaving only the outermost dams. The final boundaries of the ROI are then determined. Fig 2 shows the segmentation process of the improved watershed algorithm. If the heights of the penetrated areas are less than those of the adjacent water holes, then the water holes begin to leak. To prevent leakage from the water hole, dams are built on the boundaries of the areas. Finally, all the dams connect together and form the boundary of the segmentation.

Bottom Line: Then, an improved watershed method was used to mark suspicious areas on the CT image.Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.

ABSTRACT

Background: Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.

Method: Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.

Results: Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

No MeSH data available.


Related in: MedlinePlus