@article{2985642,
    title = "MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU",
    author = "Katsigiannis, S. and Zacharia, E. and Maroulis, D.",
    journal = "IEEE Journal of Biomedical and Health Informatics",
    year = "2017",
    volume = "21",
    number = "3",
    pages = "867-874",
    publisher = "Institute of Electrical and Electronics Engineers, Inc. (IEEE)",
    issn = "2168-2194, 2168-2208",
    doi = "10.1109/JBHI.2016.2537922",
    keywords = "Bioassay;  Biochips;  Computer architecture;  Computer graphics;  Computer graphics equipment;  Gene expression;  Image segmentation;  Microarrays;  Program processors;  Quality control, Biomedical laboratories;  Cdna microarray images;  Compute Unified Device Architecture(CUDA);  DNA micro-array;  GPU implementation;  Gridding;  Grow cuts;  User friendly interface, Graphics processing unit, complementary DNA, Article;  DNA microarray;  gene expression;  gene mutation;  genetic algorithm;  image analysis;  image quality;  image segmentation;  medical informatics;  microarray analysis;  software;  algorithm;  biology;  computer graphics;  DNA microarray;  image processing;  procedures;  software, Algorithms;  Computational Biology;  Computer Graphics;  Image Processing, Computer-Assisted;  Oligonucleotide Array Sequence Analysis;  Software",
    abstract = "Complementary DNA (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 that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) 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 user-friendly interface that requires minimum input in order to run. © 2016 IEEE."
}