Lung CT Image Segmentation Based on Combined Multi-Scales Watershed Method and Region Growing Method
In the field of computer assisted diagnostic, Image segmentation is one of the most important preprocessing steps. In this paper, the watershed algorithm segmented image by choosing different merging parameters, original image will be divided into different scales of image, High-scale image and low-scale image. High-scale image is part of the pulmonary parenchyma without the main vessel in it, low-scale image used to aid region growing tore move the branching vessels from the pulmonary parenchyma. Selected seed point located inside the branch vessels and determined the region growing threshold, with the aid of low-scale image region growing can segment the branch vessel regions .Removed the regions in the high-scale image to get the final segmentation results. In this paper, a large number of CT lung data segmentation experiment. Proven, good segmentation results are achieved.
Purpose ::: Surveillance after orchiectomy alone has become popular in the management of clinical stage I nonseminomatous germ cell testicular tumors (CSI NSGCTT), and adjuvant chemotherapy has been accepted in high-risk CSI NSGCTT. Because of the late toxicity of standard radiotherapy in CSI testicular seminoma (SGCTT), this therapeutic approach has been accepted also in the management of CSI SGCTT. In the current study, we analyzed single-center experience with risk-adapted therapeutic approaches (active surveillance and adjuvant chemotherapy) in patients with CSI SGCTT.