结合SCoW的改进FLICM医学图像分割研究

为了提高医学图像的分割精度和分割效率,针对模糊局部C-均值(fuzzy local information C-means,FLICM)系列算法分割效率低、局部空间信息描述不够准确的问题,提出结合空间约束分水岭(spatial-constrained watershed,SCo W)的改进FLICM分割算法。首先对图像进行SCo W预处理分块,压缩预处理数据;然后修正细分割处理,提取各超像素块的均值特征;最后设计一种改进的FLICM算法对各超像素块进行聚类,完成图像分割。与原FLICM算法相比,结合SCo W的改进FLICM算法的分割精度更高,分割效率得到大大提升。经理论分析和实验测试表明,该改进算法更适用于医学临床诊断的需要。

基于标记分水岭和FLICM模糊聚类的图像分割方法研究

传统的分水岭算法的应用非常广泛,但是存在过分割的问题。通常有两类方法解决该问题。第一类是后处理方法,它的原理是根据分水岭分割后的结果,使用某种方法让一些区域合并在一起。第二类属于前处理方法,在应用传统分水岭算法之前先标记提取,目前已经提出了基于标记的分水岭分割算法。这种方法虽然可以在一定程度上缓解传统分水岭算法的过分割问题,但是还是会有一定的过分割。文章在基于标记的分水岭算法的基础上,利用局部信息模糊C均值聚类算法(Fuzzy Local Information C-Means Clustering,FLICM)进行区域合并。实验结果表明:所提出的方法能有效地解决图像过分割问题,且更趋近于自然分割。

基于改进的FLICM的医学图像分割研究

针对医学图像灰度分布模糊不确定、噪声污染重等特点,提出了一种新的FLICM的改进算法,以进一步提高医学图像的分割精度和算法抗噪性。对FLICM算法严格按照梯度下降法推导获得新的隶属度和聚类中心表达式,然后设计一种充分利用像素的灰度信息和局部空间信息的FLICM改进算法。相比于原FLICM算法的医学图像分割,其抗噪性能更强,分割精度更高。理论分析和实验测试结果表明,该改进算法更适用于医学临床诊断。

A Novel Unsupervised Change Detection Method with Structure Consistency and GFLICM Based on UAV Images

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

结合模糊聚类与色彩空间的遥感图像阴影检测

高分辨率遥感图像中的阴影会导致特征信息的丢失、物体的假色调和形状失真,严重影响图像的质量。针对现有阴影检测算法对遥感图像中非匀质阴影区域及亮阴影区域易漏检、检测精度低等问题,该文结合模糊聚类及HSV色彩特征提出了一种新的遥感图像阴影检测算法。首先,对图像进行预处理,剔除亮度、饱和度与阴影区相似的干扰地物;其次,利用基于局部信息改进的模糊聚类算法对图像进行结合邻域信息的聚类,克服噪声情况下的错误聚类;最后,结合图像在HSV色彩空间的H分量和RGB空间的灰度直方图进行多阈值提取阴影,得到阴影区域。实验证明,改进算法有机结合了阴影像素点的邻域信息及色彩不变特性,在亮阴影及非匀质阴影区域检测中明显提高了检测精度。