Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets

Abstract

To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified by the level set of the pixel neighborhood, and it is therefore possible to accurately determine the contour perimeter and area of the lesion region. The results demonstrate that the value of Jaccard and Dice for benign tumors is greater than 0.99. Therefore, the segmentation effect of breast images can be achieved by utilizing a breast ultrasound image segmentation approach that uses adaptive region growth and variation level sets.