基于多样性特征与层次聚类的肺野自动分割算法
Journal: Advances in Computer and Autonomous Intelligence Research DOI: 10.12238/acair.v3i4.17877
Abstract
胸部X射线(Chest X-Ray,CXR)图像中肺野的自动分割对于肺部疾病的筛查与诊断具有重要意义。然而,CXR为投影成像,结构复杂、重叠多、边界差、自动分割难,手动勾画耗时且一致性差,故需鲁棒自动化分割方法。但深度学习黑箱性强、泛化差、计算要求高,传统无监督方法对特征与边界刻画不足。为此,本文提出融合超像素、多样性特征工程与层次聚类的分割新方法。该方法通过超像素分割降低数据维度,结合多尺度滤波、纹理分析与邻域统计增强特征判别能力,经贪心策略筛选特征以提升泛化能力与聚类效率,最终通过层次聚类合并区域并优化边界,提升分割精度与一致性。实验显示,该方法在多个公开数据集上表现优异,IoU达0.8791,优于图像检索加配准(0.8634)及OGD特征结合模糊聚类(0.7921)。
Keywords
超像素分割；多样性特征提取；层次聚类
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