南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (1): 64.
袭肖明1,2,杜亨方1,2,孟宪静1,2,张春云1,2,张 光3,于 振4,尹义龙5*
Xi Xiaoming1,2,Du Hengfang1,2,Meng Xianjing1,2,Zhang Chunyun1,2,Zhang Guang3,Yu Zhen4,Yin Yilong5*
摘要: 超声图像是乳腺癌辅助诊断常用的工具之一.肿瘤分割是乳腺超声图像分析的基础.乳腺超声图像中的灰度不同质性、纹理及形状的多变性等复杂特点使得肿瘤的精确分割较为困难.提出了一种层次化的分割框架.首先将局部灰度聚类假设引入活动轮廓模型作为底层分割模型,对图像进行初始分割;然后提出基于超像素和支持向量机(Support Vector Machine,SVM)的高层分割模型,对初始结果再进行高层分割.在高层分割过程中,首先使用简单线性迭代聚类(Simple Linear Interactive Cluster,SLIC)提取超像素,然后提取超像素的灰度、纹理和局部特征,最后使用SVM进行分类.高层分割模型是基于底层模型的分割结果学习获取的,能够检测到底层模型可能分割错误的区域,与底层模型具有较好的互补性.因此,提出的层次化分割框架具有较好的鲁棒性.在自建乳腺超声数据库上的实验结果证明了提出方法的有效性和鲁棒性.
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