Active Contour Based Lung Field Segmentation

Conventional methods that perform lung segment -ation in CT slices rely on a large contrast in hounsfield units between the lung and surrounding tissues. However, the lung fields are affected by high density pathologies, and they are discontinuities in the pixel intensities, the traditional segment- ation methods can’t get the good results. Here, we present a new segmentation method of the active contour, which is constraining with respect to a set of fixed reference shapes of lung fields. This approach is based on the shapes descriptors by the legendre moments computed from the shape regions, and it can be used in some complex lung field segmentation, especially suitable for the segmentation of lung field with the juxta-pleural pulmonary nodules. Experiments illustrate that the proposed method is able to segment the lung fields in the CT images successfully.

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Lung Field Segmentation in Chest Radiographs From Boundary Maps by a Structured Edge Detector

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2017.2687939 ◽

2018 ◽

Vol 22 (3) ◽

pp. 842-851 ◽

Cited By ~ 17

Author(s):

Wei Yang ◽

Yunbi Liu ◽

Liyan Lin ◽

Zhaoqiang Yun ◽

Zhentai Lu ◽

...

Keyword(s):

Chest Radiographs ◽

Lung Field ◽

Edge Detector ◽

Lung Field Segmentation

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Automatic Lung Field Segmentation in X-ray Radiographs Using Statistical Shape and Appearance Models

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2016.1714 ◽

2016 ◽

Vol 6 (2) ◽

pp. 338-348 ◽

Cited By ~ 15

Author(s):

Xuechen Li ◽

Suhuai Luo ◽

Qingmao Hu ◽

Jiaming Li ◽

Dadong Wang ◽

...

Keyword(s):

Lung Field ◽

X Ray ◽

Statistical Shape ◽

Appearance Models ◽

Lung Field Segmentation

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Integrated lung field segmentation of injured region with anatomical structure analysis by failure–recovery algorithm from chest CT images

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2013.10.005 ◽

2014 ◽

Vol 12 ◽

pp. 28-38 ◽

Cited By ~ 14

Author(s):

Yuma Iwao ◽

Toshiyuki Gotoh ◽

Seiichiro Kagei ◽

Tae Iwasawa ◽

Marcos de Sales Guerra Tsuzuki

Keyword(s):

Structure Analysis ◽

Ct Images ◽

Chest Ct ◽

Anatomical Structure ◽

Failure Recovery ◽

Lung Field ◽

Recovery Algorithm ◽

Injured Region ◽

Lung Field Segmentation

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Active shape model aided by selective thresholding for lung field segmentation in chest radiographs

2009 9th International Conference on Information Technology and Applications in Biomedicine ◽

10.1109/itab.2009.5394326 ◽

2009 ◽

Cited By ~ 3

Author(s):

Dimitris K. Iakovidis ◽

Michalis Savelonas

Keyword(s):

Chest Radiographs ◽

Active Shape Model ◽

Lung Field ◽

Shape Model ◽

Active Shape ◽

Lung Field Segmentation

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Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2014.2305691 ◽

2014 ◽

Vol 33 (9) ◽

pp. 1761-1780 ◽

Cited By ~ 35

Author(s):

Yeqin Shao ◽

Yaozong Gao ◽

Yanrong Guo ◽

Yonghong Shi ◽

Xin Yang ◽

...

Keyword(s):

Sparse Learning ◽

Lung Field ◽

Joint Shape ◽

Lung Field Segmentation

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Semi-automated Lung Field Segmentation in Scoliosis Radiographs: An Exploratory Study

Journal of Medical and Biological Engineering ◽

10.1007/s40846-015-0084-x ◽

2015 ◽

Vol 35 (5) ◽

pp. 608-616 ◽

Cited By ~ 2

Author(s):

Rucha Deshpande ◽

Rajkumar Elagiri Ramalingam ◽

Panagiotis Chatzistergos ◽

Vinay Jasani ◽

Nachiappan Chockalingam

Keyword(s):

Exploratory Study ◽

Lung Field ◽

Lung Field Segmentation

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Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs

Studies in Health Technology and Informatics - Public Health and Informatics ◽

10.3233/shti210219 ◽

2021 ◽

Author(s):

Satyavratan Govindarajan ◽

Ramakrishnan Swaminathan

Keyword(s):

Lung Field ◽

Statistical Features ◽

Scale Invariant ◽

Combined Features ◽

Feature Transform ◽

Automated Screening ◽

Sift Keypoints ◽

Linear Svm ◽

Scale Invariant Feature ◽

Lung Field Segmentation

In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.

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