Classification and Diagnosis of Pulmonary Nodules in Thoracic Surgery Using CT Image Segmentation Algorithm

This study was aimed at studying the pulmonary nodule (PN) classification and diagnosis through computed tomography (CT) images based on segmentation algorithms. 120 PN patients were taken as research subjects. Linear filter fine segmentation algorithm under 3D region growth was compared with the initial segmentation algorithm and applied to images of PN patients. The results showed that the segmentation effect of the proposed algorithm was at the upper-middle level. The cases of patients with smoking history were greatly more than those without (chi(2) = 1.256, P<0.05). Benign and malignant PNs were classified, and morphological features included rough ones and round-like ones. The size characteristics included edge length and area. The gray-scale features included the uniformity of the gray-scale value and the mean value of the gray-scale value. The operation time of pulmonary lobectomy (76.2 +/- 23.1 min) was obviously longer than that of pulmonary wedge resection (27.5.2 +/- 4.5 min) (P<0.05). The surgical blood loss of patients who underwent pulmonary lobectomy (125 +/- 42 mL) was remarkably higher versus patients who underwent pulmonary wedge resection (51.6 +/- 13.8 mL) (P<0.05). After the operation, the length of stay of patients who underwent lobectomy (8.6 +/- 1.4 days) was evidently longer than that of patients who underwent wedge resection (6.4 +/- 1.2 days) (P<0.05). The classification of benign and malignant PNs can effectively obtain the shape and size characteristics of PNs. Preoperative positioning surgery based on classification can shorten the operation time, reduce the amount of bleeding during the operation, and help improve the success rate of surgical resection.