Enhanced automatic lung segmentation using graph cut for Interstitial Lung Disease

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.