A segmentation method for sub-solid pulmonary nodules based on fuzzy c-means clustering

Accurately and reliably automated segmentation of pulmonary tumors could play an important role in lung cancer diagnosis and radiation oncology work. 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 proposed and tested 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 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 slices. The sub-solid nodule is then segmented and defined through 3D connected component labeling and morphological post-processing. The segmentation method was tested using a public CT dataset (LIDC) including 36 nodules. The average overlap ratio between the automated and radiologists´ segmentation of nodules is 76.18%. The false-positive ratio (FPR) and false-negative ratio (FNR) are smaller. Experimental results showed that the proposed method enabled to achieve more accurate result in segmenting sub-solid pulmonary nodules.