Criterion based thresholding algorithms are simple and effective for two-level thresholding. However, if a multilevel thresholding is needed, the computational complexity will exponentially increase and the performance may become unreliable. In this approach, a novel and more effective method is used for multilevel thresholding by taking hierarchical cluster organization into account. Developing a dendogram of gray levels in the histogram of an image, based on the similarity measure which involves the inter-class variance of the clusters to be merged and the intra-class variance of the new merged cluster . The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multilevel thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected numbers of clusters. This paper gives a comparison on Otsu’s & Kwon’s criterion with hierarchical based multi-level thresholding
