Universiti Teknologi Malaysia Institutional Repository

Enhanced automatic lung segmentation using graph cut for Interstitial Lung Disease

Ming, J. T. C. and Noor, N. M. and Rijal, O. M. and Kassim, R. M. and Yunus, A. (2015) Enhanced automatic lung segmentation using graph cut for Interstitial Lung Disease. In: 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014, 8-10 Dec 2014, Kuala Lumpur, Malaysia.


Official URL: http://www.dx.doi.org/10.1109/IECBES.2014.7047479


Radiologists are known to suffer from fatigue and drop in diagnostic accuracy due to large number of slices to read and long working hours. A computer aided diagnosis (CAD) system could help lighten the workload. Segmentation is the first step in a CAD system. This study aims to propose an accurate automatic segmentation. This study deals with High Resolution Computed Tomography (HRCT) scans of the thorax for 15 healthy patients and 81 diseased lungs segregated to five levels based on anatomic landmarks by a senior radiologist. The method used in this study combines thresholding and normalized graph cut which is a combination of region and contour based methods. The way the graph cut is implemented with a rule of exclusion can offer some knowledge for greater accuracy of segmentation. The segmentation was compared to manual tracing done by a trained person who is familiar with lung images. The segmentation yielded 98.32% and 98.07% similarity for right lung (RL) and left lung (LL). The segmentation error of Relative Volume Difference (RVD) for both RL and LL are also low at 0.89% and -0.13% respectively. The Overlap Volume Errors (OVE) are low at 3.17% and 3.74% for RL and LL. Thus the automatic segmentation proposed was able to segment accurately across right and left lung and was able to segment severe diseased lungs.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:biological organs, biomedical engineering, computerized tomography
Subjects:T Technology > T Technology (General) > T58.5-58.64 Information technology
Divisions:Razak School of Engineering and Advanced Technology
ID Code:59253
Deposited By: Haliza Zainal
Deposited On:18 Jan 2017 01:50
Last Modified:15 Sep 2021 08:22

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