Lung Segmentation by New Curve Stopping Function Using Geodesic Active Contour Model

Won, Chul Ho; Kim, Dong Hun; Lee, Jung Hyun; Yoon, K. W.; Woo, Seung Han; Yoon, Young Ho; Kim, Min Kyu; Cho, Jin Ho

doi:10.1093/ietfec/e89-a.6.1727

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Arimura et al [7] segmented lungs with a histogram‐based thresholding approach, and used morphologic operations and an image restoration technique to smooth lung contours and fill holes. Won et al [8] applied an active contour model improved with a new curve stopping function for lung segmentation of CT images. Yassine et al [9] segmented lungs from lung radiograph images.…”

Section: Introductionmentioning

confidence: 99%

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

2020

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Accurately segmenting lungs from CT images is a fundamental step for quantitative analysis of lung diseases. However, it is still a challenging task because of some interferential factors, such as juxta-pleural nodules, pulmonary inflammation, as well as individual anatomical varieties. In this study, with the combination of a superpixel approach and a hybrid model composed of convolutional neural network and random forest (CNN-RF), the authors propose a novel algorithm to segment lungs from CT images in an automatic and accurate fashion. The authors' lung segmentation covers three main stages: image preprocessing, lung segmenting and segmentation refining. A lung CT image denoised with a fractional-order grey similarity approach is first segmented to a set of superpixels, and the CNN-RF model is then employed to classify the superpixels and identify lungs from the CT image. The segmentation result is further refined by separating the left and right lungs, eliminating trachea, and correcting lung contours. Experiments show that their algorithm can generate more accurate lung segmentation results with 94.98% Jaccard's index and 97.99% Dice similarity coefficient, compared with ground truths, and it achieved better results compared with several feature-based machine learning techniques and current methods on lung segmentation.

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Section: Introductionmentioning

confidence: 99%

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

2020

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“…Then, the contour is initialized at the boundary of the chest region, which was then automatically split into two regions representing the left and right lungs. Won et al [10] segmented the lung region with a 2D geodesic active contour using the prior information about the lung area as a stopping criterion rather than the border of the lung as the external force. Shape-based techniques improve the segmentation accuracy by adding prior information about the lung shape to image signals.…”

Section: Introductionmentioning

confidence: 99%

Lung segmentation based on Nonnegative Matrix Factorization

Hosseini-Asl

Żurada

El-Baz

2014

2014 IEEE International Conference on Image Processing (ICIP)

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In this paper, a new framework for 3D lung segmentation is proposed. The primary step of this framework is to model both the spatial interaction and first-order visual appearance of the lung tissue based on a new Nonnegative Matrix Factorization (NMF) approach that has the ability to handle the inhomogeneity in the lung regions caused by arteries, veins, bronchi, and possible pathological tissues. The performance of our framework is assessed on fourteen 3D CT images. Based on the Dice Similarity Coefficient (DSC), experimental results showed that the proposed approach outperforms other lung segmentation techniques.

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