Enhanced lung segmentation in chest CT images based on kernel graph cuts
Authors: 
Xinyan Li, Shouting Feng, Daru PanAuthors Info & Claims
Xinyan Li, Shouting Feng, Daru PanAuthors Info & ClaimsPages 228 - 233
Abstract
As the most common malignant tumor, the mortality from lung cancer is increasing, so it is crucial to diagnose lung diseases in clinical evaluation. Lung segmentation is often implemented as a preprocessing step of chest Computed Tomography (CT) images according to its major role in the diagnosis of lung diseases. This paper aims to propose an efficient and accurate lung segmentation. The improved method used in this paper combines mathematical morphology and kernel graph cuts. Firstly, the noise of the lung image is filtered by homomorphic filter. And then, the kernel graph cut algorithm is used for the initial image segmentation. Finally, the mathematical morphology algorithm is adopted for post-processing. This algorithm is compared with two other approaches which are based on the maximum between-cluster variance algorithm (OTSU) and k-means clustering algorithm (KMC). Experimental results show that the proposed new segmentation approach improves the segmentation effects and performs better than the other two techniques in terms of the accuracy of the lung image segmentation.
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- Enhanced lung segmentation in chest CT images based on kernel graph cuts
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Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
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- Xidian University
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Published: 19 August 2016
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- the Supporting Plan for New Century Excellent Talents of the Ministry of Education
- the National Natural Science Foundation of China
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ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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