CAJ | 학술논문

针对含有噪声且光线不均的医学图像，提出了一种基于模糊C均值聚类的图像分割算法。模糊C均值聚类算法描述简洁、易于实现、分割效果好，在图像分割应用领域得到了快速发展，但也存在着对噪声敏感的问题。考虑到提取的医学图像数据中必定包含噪声，因此通过修改目标模糊函数J(u, v)，在引入像素点邻域信息的基础上，对邻域信息加入了惩罚因子。弥补了传统模糊C均值聚类算法的不足，使该方法对含有噪声的医学图像更加有效。实验分析表明了算法的有效性和实用性。
침대함유조성차광선불균적의학도상，제출료일충기우모호C균치취류적도상분할산법。모호C균치취류산법묘술간길、역우실현、분할효과호，재도상분할응용영역득도료쾌속발전，단야존재착대조성민감적문제。고필도제취적의학도상수거중필정포함조성，인차통과수개목표모호함수J(u, v)，재인입상소점린역신식적기출상，대린역신식가입료징벌인자。미보료전통모호C균치취류산법적불족，사해방법대함유조성적의학도상경가유효。실험분석표명료산법적유효성화실용성。
This paper proposes a new algorithm based on traditional fuzzy C-means algorithm regard to the noise and uneven light in medical images. Fuzzy C-means clustering algorithm has been rapid developed in image segmentation applications, as simple description, easy to implement, works well for segmentation. But there are also other issues such as noise sensitive. Considering that the medical images data must contain noise, a modified objective function J(u, v) has been proposed, adding a punishment factor on the basis of introducing the pixel neighborhood information. The new algorithm covers the shortage of traditional fuzzy C-means clustering algorithm, which makes the algorithm clustering with noise more effectively. Experimental results show that the algorithm is effective and practical.