In this study, the authors present a clustering-based thresholding technique for image segmentation. This technique is built on the minimum within-class variance of a scalable local region that draws upon the previous result and its spatial information to account for the connectivity between the background and the object. The cluster mean derived from the object region in each iteration is considered as an alternative global threshold to prevent the pixels with low intensity from clustering and enable the pixels with similarity to be clustered. This approach makes the method less sensitive to the problem associated with the shape of the histogram and thus leads to an automatic iterated method for a promising segmentation performance. Experiment results on synthetic and real images prove the efficiency of the method.
['Dongguo Zhou', 'Hong Zhou']
Minimisation of local within-class variance for image segmentation