融合KFCM与改进DRLSE模型的甲状腺结节图像分割

针对超声甲状腺结节图像分割问题,提出一种融合模糊核(KFCM)聚类算法与改进距离正则化水平集演化(DRLSE)模型的分割算法,解决了DRLSE模型对初始轮廓敏感、演化参数需要人工设定及分割弱边缘能力较差的问题.该算法先利用KFCM聚类算法粗分割出结节区域并二值化作为水平集初始演化轮廓,然后利用初始演化轮廓围成的区域自动计算出水平集演化参数,最后采用高斯正则化规则演化水平集分割出甲状腺结节区域.对比实验结果表明,该算法提高了甲状腺结节的分割精度,在噪声干扰下也能准确地分割出结节区域.

Image Segmentation of Thyroid Nodules Based on Fusion KFCM and Improved DRLSE Model

Aiming at the problem of thyroid nodule segmentation of ultrasound images, we proposed the new segmentation algorithm combining kernel fuzzy C means (KFCM) clustering with improved distance regularized level set evdution (DRLSE) model.The algorithm solved the problem that the DRLSE model was sensitive to initial contour, and the evolution parameters needed to be manually set, and the ability of segmentation for weak edges was poor. Firstly, the KFCM clustering algorithm was used to segment the nodule region coarsely which was regarded as theinitial evolution contour after binaryzation. Secondly, the evolution parameters of the level set were calculated automatically using the region surrounded by the initial evolution outline. Finally, the region of thyroid nodules was segmented by Gaussian regularization rule evolution level set. Comparative experimental results show that the proposed algorithm can improve segmentation accuracy ofthyroid nodules, and the nodule area can be segmented accurately even in the presence of noise.