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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

Diagram of the CAD system.
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pone.0123694.g001: Diagram of the CAD system.

Mentions: Fig 1 shows a block diagram of the CAD system based on the proposed method. The modules of the CAD system were processed consecutively.


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)

Diagram of the CAD system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390287&req=5

pone.0123694.g001: Diagram of the CAD system.
Mentions: Fig 1 shows a block diagram of the CAD system based on the proposed method. The modules of the CAD system were processed consecutively.

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