In this paper, a multi-level algorithm for Pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal and virtual shaving before the skin lesion segmentation. The NLM filter with lowest BRISQUE score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the de-noised image. RICE algorithm is used for contrast enhancement, produces enhanced images with better structural preservation and negligible loss of information. Unsharp Masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase-congruency based method in virtual shaving shows high similarity with groundtruth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework shows that, the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information and elimination of artifacts in melanoma images. Output of proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results it is found that, the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.