Automatic lung segmentation based on Graph Cut using a distance-constrained energy

Automatic lung segmentation based on Graph Cut using a distance-constrained energy

For access to this article, please select a purchase option:

Buy eFirst article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
— Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Lung segmentation serves to ensure that all the parts of the lungs are considered during pulmonary image analysis by isolating the lung from the surrounding anatomy in the image. Research has shown that computed tomography (CT) images greatly improves the accuracy of the diagnosis obtained by a physician for lung cancer detection. Therefore, inspired by the success of Graph Cut in image segmentation and given that manual methods of analysing CT images are tedious and time-consuming, an automatic segmentation method based on Graph Cut is proposed which makes use of a distance-constrained energy (DCE). Graph Cut produces globally optimal solutions by modelling the image data and spatial relationship among the pixels. However, several anatomical regions in the thoracic CT image have pixel intensity values similar to the lungs, leading to results where the lung tissue and all these regions are included in the segmentation result. The global energy function is, therefore, further constrained by using the distance of pixels from a coarsely segmented region of the CT image containing the lungs. The proposed method, utilising the DCE function, shows significant improvement over using the unconstrained energy function in segmenting the lungs from the CT images using Graph Cut.

Related content

This is a required field
Please enter a valid email address