计算机应用与软件Issue(9)：210-213,4.DOI:10.3969/j.issn.1000-386x.2015.09.050
基于自适应加权混合核函数的3D脑肿瘤分割
3D BRAIN TUMOUR SEGMENTATION BASED ON ADAPTIVE WEIGHTED COMBINED-KERNEL FUNCTION
摘要
Abstract
Aiming at the deficiency of combined-kernel function in current support vector machine (SVM),we present an adaptive weighted combined-kernel function.This kernel function is able to adaptively adjust the distance of sample points in new mapping space, changes the correction factor in sequential minimal optimisation (SMO)process to weaken the influence of penalty factor,and changes the value of Lagrange multiplier and optimises the selection of support vectors as well,so as to get a better classification interaction and to improve the classification ability of SVM.Furthermore,we propose the first time to apply the combined-kernel function SVM in brain tumour segmentation.Experimental results show that the method can more effectively segment the brain tumour.关键词
混合核函数/支持向量机/序列最小优化/修正因子/脑肿瘤磁共振图像Key words
Combined-kernel function/Support vector machine/Sequential minimal optimisation/Correction factor/Brain tumour/MRI分类
信息技术与安全科学引用本文复制引用
罗蔓,黄靖,杨丰,王晓春..基于自适应加权混合核函数的3D脑肿瘤分割[J].计算机应用与软件,2015,(9):210-213,4.基金项目
国家自然科学青年基金项目（81000642）；国家自然科学基金项目（61271155）。 （）
