  The segmentation module which precisely outlines the nodules is a crucial
step in a computer-aided diagnosis(CAD) system. The most challenging part of
such a module is how to achieve high accuracy of the segmentation, especially
for the juxtapleural, non-solid and small nodules. In this research, we present
a coarse-to-fine methodology that greatly improves the thresholding method
performance with a novel self-adapting correction algorithm and effectively
removes noisy pixels with well-defined knowledge-based principles. Compared
with recent strong morphological baselines, our algorithm, by combining dataset
features, achieves state-of-the-art performance on both the public LIDC-IDRI
dataset (DSC 0.699) and our private LC015 dataset (DSC 0.760) which closely
approaches the SOTA deep learning-based models' performances. Furthermore,
unlike most available morphological methods that can only segment the isolated
and well-circumscribed nodules accurately, the precision of our method is
totally independent of the nodule type or diameter, proving its applicability
and generality.
