cDNA microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images which often suffer from noise, artifacts, and uneven background. In this work, the MIGS-GPU (Microarray Image Gridding and Segmentation on GPU) software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a userfriendly interface that requires minimum input in order to run.
- Journal Article