
Lung nodule detection and segmentation is important for clinical diagnosis. This paper proposes a lung nodule detection and segmentation method based on a fully convolutional network (FCN), the level set method and other image processing techniques. Firstly, lung CT images are put into the FCN for lung segmentation. Secondly, lung nodules are detected inside the lung area using the threshold method and other image processing techniques. Finally, the detected lung nodules and their spiculation are segmented by the level set method and threshold method based on the coordinate system transformation. Experimental result shows that the proposed method can effectively detect and segment lung nodules with the detection accuracy of 100% and the dice overlap index of segmentation of 0.9. Therefore, this method can provide helpful references for clinical diagnosis on lung cancers.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
