激光与光电子学进展, 2020, 57 (14): 141023, 网络出版: 2020-07-28
基于SLIC和GVF Snake算法的乳腺肿瘤分割 下载: 926次
Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm
图像处理 简单线性迭代聚类 乳腺肿瘤 梯度矢量流Snake算法 图像分割 image processing simple linear iterative clustering breast tumor grandient vector flow Snake algorithm image segmentation
摘要
为了进一步提高乳腺肿瘤分割的精确度,提出了一种基于简单线性迭代聚类(SLIC)和梯度矢量流(GVF)Snake算法相结合的乳腺肿瘤分割模型。该模型首先对图像进行预处理以减少冗余信息提高后续的分割效率;其次结合图像的纹理特征提出了一种自适应K值方法,并对图像利用SLIC算法进行粗分割,描绘出乳腺肿块的初始轮廓;最后,利用GVF Snake算法加大对轮廓边缘信息的捕捉范围,进行细分割得到分割结果图。实验验证表明,该分割模型可以有效地提高分割效率和准确度,在一定程度上优于传统的分割算法,得到了较为理想的分割结果。
Abstract
In order to further improve the accuracy of breast tumor segmentation, a breast tumor segmentation model based on simple linear iterative clustering (SLIC) and grandient vector flow (GVF) Snake algorithm was proposed. The model first preprocesses the image to reduce redundant information and improve subsequent segmentation efficiency. Secondly, an adaptive value method is proposed based on the texture features of the image, and the image is roughly segmented by SLIC algorithm to describe the initial contour of the breast mass. Finally, the GVF Snake algorithm is used to increase the capture range of the contour edge information, and the segmentation result is obtained by fine segmentation. Experimental results show that the segmentation model can effectively improve the segmentation efficiency and accuracy, which is better than the traditional segmentation algorithm to some extent, and the ideal segmentation results are obtained.
王燕, 李积英, 杨宜林, 俞永乾, 王景慧. 基于SLIC和GVF Snake算法的乳腺肿瘤分割[J]. 激光与光电子学进展, 2020, 57(14): 141023. Yan Wang, Jiying Li, Yilin Yang, Yongqian Yu, Jinghui Wang. Breast Tumor Segmentation Based on SLIC and GVF Snake Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141023.