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DNA copy number aberrations are characteristic of many genomic diseases including cancer. Microrray-based comparative genomic hybridization (aCGH) is a recently developed high-throughput technique to map and detect DNA copy number (DCN) aberrations. Unfortunately, the observed copy number changes are corrupted by noise, making aberration boundaries hard to detect. They may have false positive or may miss true positive breakpoints. As a result, many approaches proposed to eliminate fluctuations on the DCN data within each aberrant interval and to preserve edges across them. In this paper, we propose a Sigma filter algorithm, because it has superior performance for denoising such data and low computational complexity. We present a comparison study between our approach and other smoothing and statistical approaches, the wavelet-based, LookAhead, CGH segmentation and HMM. Finally, we provide examples using real data sets to illustrate the performance of the algorithms.