ArticleComputational and mathematical methods in medicine2018
Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods.Annals of nuclear medicine · 2024Article
- Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection.Computational intelligence and neuroscience · 2023Article
- Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images.Journal of digital imaging · 2020Article
- Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours.Computational and mathematical methods in medicine · 2020Article
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3 authors.
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Abstract
The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography-Computed Tomography (PET-CT) images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient.
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