Fuzzy C-Means method for Colour Image Segmentation with L*U*V* Colour transformation

Fuzzy c-means clustering method for segmenting even the colour images is dealt with this work. Here the seegmentation method is based on a basic region growing method and uses membership grades’ of pixels to classify pixels into appropriate segments. The methodology incorporates the property of utilizing the extended feature space of an image to obtain better segmentation. Images which are in RGB colour space, is further processed using L*u*v* colour space, where the L*u*v* color space is derived from the CIE XYZ tristimulus values. The L*u*v* space consists of a luminosity 'L*' or brightness layer, chromaticity layer 'u*' indicating where color falls along the red-green axis, and chromaticity layer 'v*' indicating where the color falls along the blue-yellow axis. According to the image features obtained from L*u*v*, fuzzy clusters are allotted for particular colour. Fuzzy c-means algorithm segregates the image in accordance with the colour for each cluster and its neighborhood. Simulation results based on five colour test images were obtained using MATLAB.