An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm

Abstract

In medical image processing, accurate segmentation of lung tumors is very important. Computer-aided accurate segmentation can effectively assist doctors in surgery planning and treatment decisions. Although the accurate segmentation results of lung tumors can provide a reliable basis for clinical treatment, the key to obtaining accurate segmentation results is how to improve the segmentation performance of the algorithm. We propose an automatic segmentation method for lung tumors based on an improved region growing algorithm, which uses the prior information on lung tumors to achieve an automatic selection of the initial seed point. The proposed method includes a seed point expansion mechanism and an automatic threshold update mechanism and takes the combination of multiple segmentation results as the final segmentation result. In the lung image database consortium (LIDC-IDRI) dataset, we designed 10 experiments to test the proposed method and compare it with 4 popular segmentation methods. The experimental results show that the average dice coefficient obtained by the proposed method is 0.936 ± 0.027, and the average Jaccard distance is 0.114 ± 0.049. The average dice coefficient obtained by the proposed method is 0.107, 0.053, 0.040, and 0.156, higher than that of the other four methods, respectively. This study proves that the proposed method can automatically segment lung tumors in CT slices and has suitable segmentation performance.

Reliability Assessment of Wireless Sensor Networks by strain-based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box

“…For this purpose, the automatic choice of seed points for the RGA was adapted to incorporate the use of prior knowledge on lung tumor. This enhanced the segmentations accuracy of the tumor region identification, which underlines the importance to choose appropriate positions for the seed points in RGA applications [32].…”