Abstract
Currently, tuberculosis (TB) remains one of the major threats to people’s health. Specifically, the problem of under-segmentation due to adhesion of pulmonary tuberculosis lesions to the pleura is a thorny problem in image segmentation. In this paper, An effective lung parenchyma patching method is proposed, which is composed of an improved convex hull algorithm with non-uniform rational B-splines. Our method is mainly divided into three parts. First, the temporal image processing method is used to preliminarily segment the lung parenchyma. Then, the lesion area was discriminated based on the convex hull algorithm and discrete point derivative frequency. Finally, the NURBS fitting method is introduced to complete the fitting of the defect contour. According to our experimental results, the completed lesion contour blends naturally with the original lung contour. Compared with some existing algorithms, our method performs better.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (No. 61876024), and partly by the higher education colleges in Jiangsu province (No. 21KJA510003), and Suzhou municipal science and technology plan project (No. SYG202129), and Natural Science Research Fund for colleges and universities in Jiangsu Province (No. 17KJB510002).
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Shi, X., Liu, J., Xu, J., Lu, M. (2022). A Lung Segmentation Method Based on an Improved Convex Hull Algorithm Combined with Non-uniform Rational B-Sample. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_28
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