Gene microarray data are used in a large variety of applications, including phar maceutical and clinical research.By comparing gene expression in normal and ab normal cells, microarrays can be used for identifying genes involved in particular diseases, and then these genes can be targeted by therapeutic drugs.Most gene ex pression data are produced from spotted microarray images.A spotted microarray image consists of thousands of spots, with individual DNA sequences first printed at each spot and then equal amounts of probes (e.g., cDNA samples) from treatment and control cells mixed and hybridized with the printed DNA sequences.To obtain gene expression data, the image needs to be segmented first to separate foregrounds from backgrounds for individual spots, and then averages of foreground pixels are usedfor computing the gene expression data.So, image segmentation of microar ray images is related directly to the reliability of gene expression data.Several image segmentation procedures have been suggested and included in some software packages handling gene microarray data.In this talk, we discuss a new image seg mentation methodology proposed recently by Qiu and Sun (2007, JASA, December issue, 1129-1144), and a post-processing procedure, both of which are based on lo cal linear kernel smoothing.Theoretical arguments and numerical studies show that they work well in applications.