+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Adaptive border marching algorithm: automatic lung segmentation on chest CT images

Adaptive border marching algorithm: automatic lung segmentation on chest CT images

Computerized Medical Imaging and Graphics 32(6): 452-462

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 051368964

Download citation: RISBibTeXText

PMID: 18515044

DOI: 10.1016/j.compmedimag.2008.04.005

Related references

Adaptive fast marching method for automatic liver segmentation from CT images. Medical Physics 40(9): 091917, 2013

Automatic segmentation of lung fields on chest radiographic images. Computers and Biomedical Research An International Journal 32(3): 283-303, 1999

Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Computer Methods and Programs in Biomedicine 143: 1-12, 2017

Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images. Computerized Medical Imaging and Graphics 30(5): 299-313, 2006

A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images. Medical Physics 22(2): 183-191, 1995

Integrated lung field segmentation of injured region with anatomical structure analysis by failurerecovery algorithm from chest CT images. Biomedical Signal Processing and Control 12: 28-38, 2014

Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching. International Journal of Computer Assisted Radiology and Surgery 12(4): 581-593, 2017

Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network. Biomed Research International 2016: 3219068, 2016

A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images. Computers and Electronics in Agriculture 140: 290-302, 2017

Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification. Asian Pacific Journal of Cancer Prevention 18(12): 3395-3399, 2017

Automatic 3-D segmentation of endocardial border of the left ventricle from ultrasound images. IEEE Journal of Biomedical and Health Informatics 19(1): 339-348, 2015

A fully automatic algorithm for segmentation of the breasts in DCE-MR images. Conference Proceedings 2010: 3146-3149, 2010

An adaptive algorithm for the automatic segmentation of continuous stuttered speech. Biomedical Sciences Instrumentation 30: 147-152, 1994

An automatic 2D CAD algorithm for the segmentation of the IMT in ultrasound carotid artery images. Conference Proceedings 2009: 515-519, 2009

Automatic initialization algorithm for carotid artery segmentation in CTA images. Medical Image Computing and Computer-Assisted Intervention 8(Pt 2): 846-853, 2005