Ground-glass opacity nodules detection and segmentation using the snake model

Bong, Chin Wei; Liew, C.C.; Lam, Hong Yoong

doi:10.1016/b978-0-12-804536-7.00005-3

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…To evaluate our proposed method, we implemented the CV model [29] , the normalized CV (NM-CV) model [38] , [39] , and the snake algorithm [40] , and compared the results with those of our proposed method, based on chest CT images including juxta-pleural nodules ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N = 314$ \end{document} ) from 42 subjects, and all chest CT images ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N = 16,873$ \end{document} ) from 84 subjects. For the snake algorithm [45] , [46] , the parameters of length ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\alpha $ \end{document} ), curvature ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta $ \end{document} ) and smoothing spline ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\lambda $ \end{document} ) were used with 0.001, 0 and 100, respectively in [46] .…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we investigated whether the separated groups in the difference image comprised juxta-pleural nodules or portions of the lung wall using concave point detection and circle/ellipse Hough transform. We evaluated the performance of our proposed method by comparing it with the CV model [29] , the normalized and modified CV (NM-CV) method [38] , [39] , and the snake model [40] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach

Chung

Jeon

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Objective: chest computed tomography (CT) images and their quantitative analyses have become increasingly important for a variety of purposes, including lung parenchyma density analysis, airway analysis, diaphragm mechanics analysis, and nodule detection for cancer screening. Lung segmentation is an important prerequisite step for automatic image analysis. We propose a novel lung segmentation method to minimize the juxta-pleural nodule issue, a notorious challenge in the applications. Method: we initially used the Chan–Vese (CV) model for active lung contours and adopted a Bayesian approach based on the CV model results, which predicts the lung image based on the segmented lung contour in the previous frame image or neighboring upper frame image. Among the resultant juxta-pleural nodule candidates, false positives were eliminated through concave points detection and circle/ellipse Hough transform. Finally, the lung contour was modified by adding the final nodule candidates to the area of the CV model results. Results: to evaluate the proposed method, we collected chest CT digital imaging and communications in medicine images of 84 anonymous subjects, including 42 subjects with juxta-pleural nodules. There were 16 873 images in total. Among the images, 314 included juxta-pleural nodules. Our method exhibited a disc similarity coefficient of 0.9809, modified hausdorff distance of 0.4806, sensitivity of 0.9785, specificity of 0.9981, accuracy of 0.9964, and juxta-pleural nodule detection rate of 96%. It outperformed existing methods, such as the CV model used alone, the normalized CV model, and the snake algorithm. Clinical impact: the high accuracy with the juxta-pleural nodule detection in the lung segmentation can be beneficial for any computer aided diagnosis system that uses lung segmentation as an initial step.

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