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

© The Institution of Engineering and Technology
Received 14/05/2017, Accepted 05/02/2018, Revised 10/12/2017, Published 06/02/2018

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.

Inspec keywords: medical image processing; computerised tomography; image segmentation; lung; image resolution

Other keywords: DCE; global energy function; pulmonary image analysis; Graph Cut; distance-constrained energy; automatic lung segmentation; pixel intensity values; computed tomography images; automatic segmentation method

Subjects: X-rays and particle beams (medical uses); X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Biology and medical computing; Optical, image and video signal processing; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation

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