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This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

The main objective of this paper is to automate segmentation and analyze nucleolus to cytoplasm ratio of cervical cell. The first step, the nuclei initialization, concentrates on the target nuclei and related boundaries by removing incidental regional minima from the gradient map. The multi-scale gradient watershed transform is used for nuclei segmentation. It identifies regional minima and preserves the valid nuclei. A local filter is designed for the segmentation of cytoplasm. The performance of the proposed method measuring Nuclear to Cytoplasm (NC) shows better results as compared to other any other methods available in the literature.