Unsupervised segmentation of OSF by fusion of RGA and DCT with contextual information

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.

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