Accurately and reliably automated segmentation of pulmonary tumors could play an important role in lung cancer diagnosis and radiation oncology. However

it remains a very difficult task in particular for segmenting pulmonary tumors associated with sub-solid nodules that are partially obscured in lung CT images. In this study

we propose and test an improved weighed kernel fuzzy C-means (IWKFCM) method that incorporates vessels structure information and classes’ distribution as weights to segment sub-solid pulmonary nodules. For this purpose

a region of interest (ROI) of a nodule in center CT slice is manually defined. The IWKFCM algorithm is applied to identify and cluster the potential nodule pixels located in this manually-defined center slice and its adjacent (surrounding) slices. The sub-solid nodule is then segmented and defined through 3D connected component labeling and morphological post-processing. This segmentation method is tested using two datasets including 36 nodules selected from a public dataset (LIDC) and 18 nodules depicted on CT images collected from our local hospital. The average overlap ratios between the automated and radiologists’ segmentation of nodules of two datasets are 76.18% and 71.65% respectively. In both datasets

the false-positive ratio (FPR) and false-negative ratio (FNR) are smaller than 17%. Experimental results show that the proposed method enables us to achieve more accurate result in segmenting sub-solid pulmonary nodules than the other previously reported clustering methods. The segmentation results could also provide a consultative reference for more accurately extracting image features and optimal classification of pulmonary nodules in developing computer-aided detection (CAD) schemes.