CAJ | 학술논문

针对传统分割方法在分割数量上的限制,以及模糊聚类方法在层次优化上的不足,提出一种有效的基于多尺度连接模型的人脑磁共振图像模糊分类算法.对脑磁共振图像进行灰度不均匀性校正后,该方法通过非线性扩散连接模型引入尺度间的模糊约束,定义2个模糊距离,并应用到模糊聚类算法中,组合尺度间和尺度内的模糊约束,给出一个多分辨模糊聚类框架.实验结果表明了该算法的准确性和有效性.
침대전통분할방법재분할수량상적한제,이급모호취류방법재층차우화상적불족,제출일충유효적기우다척도련접모형적인뇌자공진도상모호분류산법.대뇌자공진도상진행회도불균균성교정후,해방법통과비선성확산련접모형인입척도간적모호약속,정의2개모호거리,병응용도모호취류산법중,조합척도간화척도내적모호약속,급출일개다분변모호취류광가.실험결과표명료해산법적준학성화유효성.
To make up for the restriction of traditional segmentation methods regarding the number of segmentation and fuzzy clustering method's lacking in levels of optimization,this paper presents an effective fuzzys egmentation approach based on multiacale linking model for brain MRI.The non-uniformity of gray-scale is corrected after analysis of the bias in brain MRI.A fuzzy inter-scale constraint via antistrophic diffusion linking model is introduced.Two fuzzy distances are developed and embedded into the fuzzy clustering algorithm.Moreover,a multiresolution framework combining the inter-scale and intra-scale constraints is presented.Experimental results show the accuracy and validity of this method in which a large deal of brain MRI data is used.