ABSTRACT
Microarray images are becoming increasingly important in bioinformatics, proteomics, and in the development of patient-specific therapies. The compression, processing, and analysis of these images are relatively new topics of research. In this paper, we focus on microarray image compression using singular value decomposition (SVD), a well known information compaction method. Although the SVD algorithm produces significant compression results, modifications may lead to further improvements. In an attempt to increase the compression ratio while maintaining a high peak signal-to-noise ratio, we adopt a subdivision scheme wherein the modified SVD is applied on each subimage. Experimental results indicate that SVD approaches are promising in compression, and may also lead to improved post-processing operations and analysis techniques.
Subject(s)
Data Compression/methods , Image Enhancement/methods , Image Interpretation, Computer-Assisted/methods , In Situ Hybridization, Fluorescence/methods , Microscopy, Fluorescence/methods , Oligonucleotide Array Sequence Analysis/methods , Pattern Recognition, Automated/methods , Algorithms , Gene Expression Profiling/methodsABSTRACT
PURPOSE: To evaluate the accuracy and efficiency of rigid-body registration of two-dimensional fast cine and real-time cardiac images to high-resolution and SNR three-dimensional preprocedural reference volumes for application during MRI-guided interventional procedures. MATERIALS AND METHODS: Mutual information (MI) and correlation ratio (CR) similarity measures were evaluated. The dependence of registration accuracy and efficiency on different resolution and SNR parameters, and also on cardiac-phase differences was evaluated in a porcine model. Two-dimensional images were initially misoriented at distances (d) of 2-10 mm, and rotations of +/-5 degrees about all axes. Registration error and computation time were evaluated, and performance was also assessed visually. RESULTS: The maximum registration error using MI (<2.7 mm and <3.6 degrees ) occurred for d = 10 mm, misrotation of +/-5 degrees , and relative SNR = 1. The computation time was 15 seconds for MI and 10 seconds for CR. CONCLUSION: Registration accuracy was not highly dependent on the relative timing, within the cycle, between the two-dimensional and three-dimensional images. Registration using CR was faster than that using MI, although accuracy was marginally higher with MI. J.
