Ultrasound image segmentation algorithm based on global and local correntropy K-mean active contour model
KHUMDOUNG Netsai
QIU Tianshuang
Abstract：The local correntropy K-mean (LCK) model has a better image segmentation effect when the non-Gaussian noise and the image grayscale inhomogeneous,but the computational complexity is rather high and the convergence is rather slow.In order to solve this problem, a global and local correntropy K-mean (GLCK) image segmentation algorithm was proposed by combining the lo-cal correntropy and the global correntropy energy.The local correntropy force played a leading role near the target boundary,which was used to attract the level set function curve to reach the target boundary,and the global correntropy force played the leading role at the distance from the target boundary.The image segmentation experiments of ultrasonic medical images and synthetic images were carried out and compared with the LCK and other models.The results show that the proposed GLCK model has better robustness and image segmentation precision, and the calculation time is also significantly reduced.On the other hand, the GLCK model has a good segmen-tation effect for ultrasound medical images with severe noise and blurred boundaries.
Keywords：Ultrasound imageImage segmentationActive contour modelCorrentropy-based K-meansLevel set method
Publication Date：2018-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 142-147 )