基于改进型FCM聚类算法的彩色牛肉图像中筋膜区域分割方法研究

A Segmentation Method for Fascia Recognition in Color Beef Ribeye Image Based on Modified Fuzzy C-Means Clustering Algorithm

介绍一种基于HSI颜色空间的改进型FCM聚类算法,并将此算法运用于牛肉眼肌切面图像中筋膜区域的分割。该方法根据牛肉眼肌切面图像像素分布的特点,将H分量和I分量作为聚类分割算法的特征向量,改进了FCM聚类算法初始聚类中心的选取方法以及距离度量公式。结果表明:所提出的算法在保证FCM算法本身收敛速度快的同时,也提高了图像区域分割的精度和准确性,对噪声具有较强的抑制能力。

A modified fuzzy c-means clustering algorithm based on HSI color space was proposed and introduced for the separation of fascia region in color beef ribeye image. In this method, the H component and I component were used as feature vectors according to the distribution characteristics of beef ribeye image pixels to modify initial cluster center selection and distance-weight formula for FCM clustering algorithm. The results obtained showed that the proposed HSI algorithm could ensure fast convergence features of FCM clustering and increase the precision and accuracy of image segmentation, and had strong noise-suppressing capability.