Unsupervised segmentation of OSF by fusion of RGA and DCT with contextual information
by Tathagata Ray, Anirban Mukherjee, J. Chatterjee, R.R. Paul, Pranab K. Dutta
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 4, No. 2, 2010

Abstract: The aim of this paper is to segment Light Microscopic (LM) images of Oral Sub-mucous Fibrosis (OSF) into its constituent layers. In this regard, fusion of features based on Region Growing Algorithm (RGA) and context-enhanced rotational invariant Discrete Cosine Transform (DCT) has been studied. The overall segmentation accuracy of this fused method is higher than that of context-enhanced DCT-based method. Fusion of features based on different methods often eliminates the disadvantages and utilises the advantages of individual method. Fuzzy c-means clustering has been found to be little ahead of k-means clustering in terms of segmentation accuracy.

Online publication date: Sat, 07-Aug-2010

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