In recent years, due to high application of genetics in various fields such as medicine, there have been lots of efforts for improving this field. One of these developments is microarray technology which nowadays has an increasing application in genetics and medicine. Microarray technology is a new tool in genetics and a way to study the behavior of genes in biotechnology research, which allows study of thousands of genes simultaneously. The increasing trend towards using microarray technology results in producing large number of images for each experiment. Abundance of these images, their large volume and the need for long-term maintenance and storage necessitates their compression. Hence real-time compression of these images is one of the main bottlenecks in this technology. In this thesis, two new architectures for lossless microarray image compression are proposed. The proposed architectures are based on pipeline. The first proposed architecture is asynchronous and based on foreground-background separation. The second one compresses microarray images using wavelet transforms. Both architectures improve compression ratios in comparison with the previous methods and structures.