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Curvelet transform is a new extension of wavelet transform which aims to deal with interesting phenomena occurring along curves. Curvelet transform is particularly a challenging task to classify human organs in CT scans using gray-level information. An efficient implementation of curvelet transform for medical image segmentation and denoising has been presented in this paper. A comparison study has been carried out in this paper between different transforms which reveals that curvelet transform exhibits optimal representation of the region of interest (ROI) with better accuracy and less noise.