Computer Aided Diagnosis (CAD) of CT lung image has been a revolutionary step in the early diagnosis of diseases present in the lung. Developing an efficient and robust algorithm for Lung computer tomography (CT) image segmentation has been a demanding area of growing research of interest during the last two decades. The initial step in computer aided diagnosis of lung CT image is generally to segment the Region of Interest (ROI) present in it and then to analyze each area separately inorder to

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... find the presence of pathologies present in it. This research reports on segmentation of the ROI by segmenting the CT lung images using supervised contextual clustering along with the combination of region growing algorithm. Region growing has been combined with CC in this work since it reduces the number of steps in segmentation for the process of identifying a tissue in the CT lung image. The performance of this proposed segmentation is proved to be better when it is compared with other existing conventional segmentation algorithms like 'Sobel', 'Prewitt', 'Robertz', 'Log', 'Zerocross'. From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy.