2023, Vol. 27 ›› Issue (2): 171-176

Application of improved geodesic active contour model in kidney CT image segmentation

Quan Meilin1, Liu Qi1, Chen Xi2, Deng Xiaobo2, He Kechen2, Liu Yanli3   

Abstract:

BACKGROUND: Kidney CT image with poor quality shows similar gray scale to that of surrounding tissues on abdominal CT images. Therefore, it is difficult to segment the kidney accurately by traditional image segmentation method.
OBJECTIVE: To assist the diagnosis of renal diseases and improve the accuracy of renal segmentation in CT images based on an improved geodesic active contour model.
METHODS: Based on the comparative analysis of various traditional medical image segmentation algorithms, a kidney segmentation algorithm based on the improved geodesic active contour model was designed. The region of interest was delineated according to prior knowledge, and the initial contour of the kidney was obtained during the pretreatment stage. Based on the geodesic active contour model of the level set method, the boundary response of the kidney region was enhanced and the improved edge indicator function was used to make the contour curve evolution result closer to the real target boundary.
RESULTS AND CONCLUSION: The mean Dice coefficient and mean overlap degree of 328 two-dimensional CT images of the kidney were 0.974 9 and 0.907 1, respectively, which were improved compared with other level set methods. Experimental results show that this model can improve the segmentation accuracy and efficiency of the kidney region in abdominal CT images.
Key words: medical image processing, computer-aided diagnosis, level set, image segmentation, geodesic active contour