Adaptive Image Enhancement for Fluorescence Microscopy

Fluorescent cell micrographs contain inhomogeneous contrast levels due to fluorescence intensity variations and the existence of out-of-focus objects. A novel image enhancement method based on adaptive local region sizes is proposed, which can correctly highlight salient objects from changing background intensity. The local region size for each pixel is determined adaptively, then locally normalized intensity values based on the regions are obtained, automatically taking inhomogeneous contrast and background intensity into account. Object background binarization can be done by applying simple thresholding to the enhanced image. The method is validated by comparison with ground truth segmentations of cell micrographs, which shows that after applying the proposed signal enhancement, binarization using common global thresholding methods produces segmentations much closer to the ground truth. Segmentations produced by the proposed method and simple global thresholding are also compared to some recent adaptive segmentation methods, showing that the proposed method is more efficient and appropriate for cell micrograph analysis.