Analysis and processing of HRCT images of the lung for automatic segmentation and nodule detection
Thesis DisciplineComputer Science
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
Automatic lung segmentation and lung nodule detection through High- Resolution Computed Tomography (HRCT) image is a new and exciting research in the area of medical image processing and analysis. In this research, two new techniques for segmentation of lung regions and extraction of nodules on the HRCT image are proposed. An automatic lung segmentation system is proposed for identifying the lungs in HRCT lung images. First, lung regions are extracted from the HRCT images by grey-level thresholding. The lung background information is eliminated by linear scans originating from border pixels. Finally, lung boundaries are smoothed along the mediastinum. The lung nodule extraction from the HRCT image is processed based on a set of continuous HRCT slices of lung images. In the first stage, the abnormal areas are extracted based on nodule pixel collection and combination. In the final stage, the abnormal area is extracted by comparing the density and shape profile. Both of the systems have been tested by processing data sets from 10 continuous image sets (100 images). Lung segmentation results are presented by comparing our automatic method to manually traced borders. Averaged over all results, the accuracy of lung segmentation is 96.10%. The proposed nodule detection method has been tested on image sets containing healthy and unhealthy lung images. Statistical analysis has been done and the results show the overall nodule detection rate is 88.44% along with the false positive rate of 0.18.