# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 3 Detection of CISs Based on Traditional Machine Learning Methods
### Candidate Lesion Segmentation  
Before identifying the lesion and inputting it for further processing in the process stage, some works will segment the lesion region as a candidate. Candidate lesion region segmentation approaches for lung CIS detection may be split into two categories: threshold methods and mathematical morphological methods. The main idea of the thresholding method is to select a suitable threshold value to determine the classification of each pixel in the image according to the difference between the CT value of the lesion region and the normal tissue, and to guide the segmentation process to complete the segmentation of the candidate region. Due to the disparity in density of various tissues, a substantial number of false positive regions containing blood vessels will be formed by applying a single threshold to segment lesions. Therefore, researchers generally utilize a multilevel thresholding strategy to segregate potential locations. [PERSON] [12] used K-means clustering to create global thresholds using the L2G (local-to-global multithreshold segmentation) method, and the proposed multithreshold segmentation approach increased the detection performance of GGO signs. [PERSON] [13] proposed an improved adaptive multilevel threshold segmentation approach on the basis of this research, which could identify not only focal GGO but also diffuse GGO and substantially increase the robustness of GGO sign identification. While the multilevel thresholding segmentation algorithm can reduce the false positive rate, the application of the multilevel threshold method will still generate a large number of false positive regions consisting of blood vessels and bronchi when some signs are highly close to the gray values of lung vessels and bronchi. The morphology approach relies on set theory and mathematical operations, which measure and extract the corresponding shape features according to the structure of images. The essential notion of this approach lies in the selection of structural elements, which may be employed to complete different image analyses and create alternative structures. [PERSON] [14] proposed a technique based on differential geometry to compute spiculation, lobulation, and spherical signs using segmented nodule masks of diverse morphologies. However, the approach is poorly generalized due to the complexity and diversity of CISs structures and the impossibility to compute numerous indications using a single structural element, and the method is only relevant to images of lesions with considerable variations in gray values at edge locations. In addition, the morphological open-close operation can overcome the problem of a significant number of false positive regions produced by the inability of the threshold approach to correctly split lesions and vessels. [PERSON] [15] used morphological methods to recognize calcification signs, smoothed the boundaries of the obtained candidate areas, applied open operation to select corrosion strength, disconnected narrow areas and eliminated protruding areas, and used close operation to fill the cavity to better segment calcified and noncalcified areas and reduce the generation of false positive areas during lesion segmentation. The contrast analysis of the threshold method and the mathematical morphology method revealed a comparable amount of research using both types of methods for lung sign segmentation, which is characterized by combining digital image processing methods and machine learning methods for CISs segmentation. Since pulmonary lesions may contain more adherent blood vessels, the threshold approach is more likely to create false positive regions compared to the mathematical morphology method, and its segmentation performance needs to be improved. In addition, the results of this research cannot be compared directly due to variances in the kind and quantity of CISs investigated.