An active lattice model in a Bayesian framework
Computer Vision and Image Understanding
Microarray image compression: SLOCO and the effect of information loss
Signal Processing - Special issue: Genomic signal processing
A Markov Random Field model of microarray gridding
Proceedings of the 2003 ACM symposium on Applied computing
Lossless and Lossy Compression of DNA Microarray Images
DCC '04 Proceedings of the Conference on Data Compression
A Foreground/Background Separation Algorithm for Image Compression
DCC '04 Proceedings of the Conference on Data Compression
EURASIP Journal on Applied Signal Processing
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
MACE: lossless compression and analysis of microarray images
Proceedings of the 2006 ACM symposium on Applied computing
On denoising and compression of DNA microarray images
Pattern Recognition
Information Sciences: an International Journal
Robust pre-processing and noise reduction in microarray images
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Microarray subgrid detection: a novel algorithm
International Journal of Computer Mathematics - Bioinformatics
Complementary DNA microarray image processing based on the fuzzy Gaussian mixture model
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
A bio-inspired CNN with re-indexing engine for lossless DNA microarray compression and segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Spot addressing for microarray images structured in hexagonal grids
Computer Methods and Programs in Biomedicine
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With the recent explosion of interest in microarray technology, massive amounts of microarray images are currently being produced. The storage and the transmission of this type of data are becoming increasingly challenging. Here we propose lossless and lossy compression algorithms for microarray images originally digitized at 16 bpp (bits per pixels) that achieve an average of 9.5 - 11.5 bpp (lossless) and 4.6 - 6.7 bpp (lossy, with a PSNR of 63 dB). The lossy compression is applied only on the background of the image, thereby preserving the regions of interest. The methods are based on a completely automatic gridding procedure of the image.