IEEE Transactions on Pattern Analysis and Machine Intelligence
LOCO-I: a low complexity, context-based, lossless image compression algorithm
DCC '96 Proceedings of the Conference on Data Compression
Wavelet thresholding via MDL for natural images
IEEE Transactions on Information Theory
Gridding and Compression of Microarray Images
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
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
EURASIP Journal on Applied Signal Processing
Noise reduction of cDNA microarray images using complex wavelets
IEEE Transactions on Image Processing
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Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission require compression that takes advantage of microarray image structure. In this paper we develop a compression scheme for microarray images which can be either lossless or lossy with successive refinements. Existing measures of distortion such as mean squared pixel-wise error and visual fidelity are not appropriate for microarray images. We introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. Furthermore, our scheme has a coded data structure that allows fast decoding and reprocessing of image sub-blocks, and includes summary statistics and image segmentation information. The average lossless compression ratio is 1.83:1 for our cDNA test images and 2.43:1 for our inkjet test images, comparable or better than state-of-the-art lossless schemas, yet with additional structure and information. At an average lossy compression ratio of 8:1 for cDNA microarrays, we find that our scheme minimizes the effects of compression loss compared to other algorithms. We show that the variability in differential gene expression levels extracted from lossily vs. losslessly compressed microarray images is less than both the variability between different arrays and the variability between different extraction algorithms. In fact, lossy compression can improve the estimation of gene expression levels for cDNA images.