This study presents a novel strategy to characterize and remove non-nuclei signal (noise) in histopathological images stained with hematoxylin and eosin (H and E), a preprocessing step to improve traditional nuclei segmentation methods. Any non nuclei structure is mapped to a noiselet space at different resolution levels where a classic classifier is trained to recognize the noiselet coefficients of this projection. The proposed approach was evaluated with two multi-organ datasets manually annotated, comparing the nuclei segmentation obtained by a watershed algorithm plus the presented approach against the watershed method alone. An average Dice Score in these datasets (MICCAI Challenge and TCIA) of 70.2 and 59.6 was obtained by applying the herein introduced method, while the obtained Dice Score with only the watershed method was of 66.7 and 55.5.