Image analysis – Image compression or coding – Pyramid – hierarchy – or tree structure
Reexamination Certificate
2002-01-14
2003-06-17
Young, Brian (Department: 2819)
Image analysis
Image compression or coding
Pyramid, hierarchy, or tree structure
C382S275000, C382S250000
Reexamination Certificate
active
06580831
ABSTRACT:
FIELD OF THE INVENTION
The invention relates generally to micro sensors and, more particularly, to micro sensor signal data processing.
BACKGROUND OF THE INVENTION
Micro sensors and, more particularly, biosensors have attracted much attention lately due to their increasing utility in the pharmaceutical, chemical and biological arenas. Biosensors have been developed to detect a variety of biomolecular complexes including oligonucleotide pairs, antibody-antigen, hormone-receptor, enzyme-substrate and lectin-glycoprotein interactions and protein interactions, for example. In general, biosensors are comprised of two components: a molecular recognition element and a transducing structure that converts the molecular recognition event into a quantifiable signal. Signal transductions are generally accomplished with electrochemical, field-effect transistor, optical absorption, fluorescence or interferometric devices.
Generally, an array of biosensors is used for the execution of biomedical and biomolecular measurements in which the state of the biological system is translated into a response at a specific sensor location. Biomolecular sensor arrays are comprised of individual sensors cells organized in some fashion, such as on a rectangular grid. The output of the biomolecular sensor array is multidimensional data in which each sensor cell (i.e. each data point in the array) codes the response of a specific experiment.
An Optical Readout Biomolecular Sensor (ORBS) array is an example of a specific type of biomolecular sensor. With an ORBS, the state of the biological system is translated into an optical response at a specific sensor location. Protein microarrays are an example for ORBS's. The data at the output of the ORBS are multidimensional data similar to image data, containing a defined spatial sequence of blots with values that differ from the image background (see FIG.
1
). The blots code the system response using intensity and color. The geometry in the spatial arrangement of the blots correspond to the experimental condition, i.e. blots can be assigned to an event to be measured. The number of blots, i.e., of events to be measured can be large, e.g. 10000.
The resulting data are usually stored as image data, i.e. as multidimensional pixel arrays with a sufficient resolution, e.g. 24 bpp (bits per pixel). An example size for such an pixel array is. 64 MB. The massive application of ORBS's generates large amounts of data that are difficult to store and distribute. In an effort to coupe with this problem, data compression is used to considerably reduce the number of bits to be stored or transferred while retaining the information content in the data. Data compression is an important consideration for efficient storage of ORBS's data and for transfer of such data over the internet and/or wireless applications, for example.
Currently, there exists no compression standard for this type of data. In “‘Comprestimation’: Microarray Images in Abundance”, by Rebecks Jornsten and Bin Yu, 2000 Conference on Information Sciences and Systems, Princeton University, Mar. 15-17, 2000, which is hereby incorporated by reference, there is described a proposal for “compression” schemes for data from a subclass of ORBS, namely cDNA microarrays, based on (1) predictive coding in real-space and (2) transform-coding using Mallat's orthogonal critically-sampled separable wavelets. Mallat's orthogonal critically-sampled separable wavelets is described in “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”, by Stephane G. Mallat, IEEE Transaction on Pattern Analysis and Machine Intelligence, Volume 11, Number 7, pages 674-693, July 1989. The aforementioned “compression” schemes for cDNA microarrays are insufficient for considerably reducing the number of bits to be stored or transferred while retaining the information content of the data in real environments (which generally exhibit noisy data signals) and generally produce directional compression artifacts due to the directional anisotropy in this signal transform.
Application of off-the-shelf image compression methods, such as “jpeg”, “tif” or “Lempel-Ziv” have also been proposed. Application of image signal-transform-based compression methods, such as “jpeg” or “tif”, results in poor compression rates (generally less than 5) and/or loss of information in the data since such compression methods are optimized for visual reproducibility of natural images rather than for numerical reproducibility of data features. Textual compression methods, such as “Lempel-Ziv”, do not suffice because of associated low compression factors due to the non-textual nature of ORBS data.
SUMMARY OF THE INVENTION
The present invention achieves technical advantages as a system and method for compression of image data from an Optical Readout Biomolecular Sensor array while preserving the usable information and eliminating or reducing associated noise in which the image data includes a signal and noise. The image data is transformed using a multiscale transform technique (such as the Pyramidal Median Transform) such that the image data is represented as a plurality of transform coefficients each having a corresponding weight. The respective weights are used to determine those transform coefficients associated with noise. The transform coefficients determined to be associated with noise are extracted from the original plurality of transform coefficients. The remaining transform coefficients are subsequently quantized and coded.
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patent: 4751742 (1988-06-01), Meeker
patent: 6353686 (2002-03-01), Daly et al.
Stephane G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,”IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 11, No. 7, pp. 674-693, Jul. 1989.
Rebecka Jörnsten and Bin Yu, “‘Comprestimation’: Microarray Images in Abundance”,2000 Conference on Information Science and Systems, Princeton University, pp. 1-6, Mar. 15-17, 2000.
J.-L. Starck, F. Murtagh, and M. Louys, “Astronomical Image Compression Using the Pyramidal Median Transform”,Astronomical Data Analysis Software and Systems IV, ASP Conference Series, vol. 77, pp. 1-4, 1995.
Infineon - Technologies AG
Jackson Walker L.L.P.
Nguyen John
Young Brian
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