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"Density-Based Method of Comparing Images and Detection of Morphological Changes Using the Method Thereof" in Patent Application Approval Process

July 1, 2014



By a News Reporter-Staff News Editor at Information Technology Newsweekly -- A patent application by the inventors Schauer, Kristine (Paris, FR); Duong, Thanh Buu (Paris, FR); Goud, Bruno (Paris, FR), filed on July 19, 2012, was made available online on June 19, 2014, according to news reporting originating from Washington, D.C., by VerticalNews correspondents.

This patent application is assigned to Centre National De La Recherche Scientifique Cnrs.

The following quote was obtained by the news editors from the background information supplied by the inventors: "In many technical fields, the problem of comparing data samples has attracted much research to investigate its theoretical and practical aspects.

"Historically, the first comparison methods involved small computational burdens. For instance, the so-called 't-test' relied on fitting normal distributions having equal variance values but different mean values, thus reducing the original problem of data comparison to a comparison for a difference between the mean values.

"However, such a t-test test is limited, because if the marginal variance values are not equal, even approximately, it can give erroneous statistical significance results.

"On the other hand, while more sophisticated parametric tests have also been introduced, such parametric tests do not overcome the basic problem of pre-specifying the parametric form.

"There exist also non-parametric tests, such as the Mann-Whitney, Kolmogorov-Smirnov and Wald-Wolfowitz tests. The first of these tests is based on the ranks from the combined samples, the second on the supremum distance between two distribution functions, and the third on the consecutive runs of membership from the two samples. However, such univariate tests apply only for 1-dimensional continuous data and cannot apply to multivariate data.

"When it comes to tests for multivariate data, one approach is based on data depth as a multivariate analogue of ranking, but such approach has not met the same wide acceptance as the above-mentioned univariate tests, because the former have not consistently yielded intuitive inferences when applied to experimental data.

"Testing multivariate date can be achieved by computationally intensive resampling methods, however a second major trade-off is that they require sufficient familiarity as resampling requires calibration for each data analysis situation at hand.

"In the field of comparing cellular endomembrane organization, a resampling strategy has been described in 'Probabilistic density maps to study global endomembrane organization', Schauer, Duong et al., NATURE METHODS, 30 May 2010, wherein a statistical analysis using non-parametric Kernel density estimators is used.

"Such a technique has proven to be a more flexible procedure, at the cost of an increased computational burden due to the calculation of the critical quantiles of the null distribution via resampling, which requires calibration for each data analysis situation at hand. Such constraints prevent the wider use of bootstrap density-based two-sample tests outside the computational statistical community. In particular, these tests are not easily available to biologists."

In addition to the background information obtained for this patent application, VerticalNews journalists also obtained the inventors' summary information for this patent application: "It is thus an object of the present invention to overcome the above-identified difficulties and disadvantages by providing a multivariate data samples comparison method, which does not unduly increase the computational burden and remains accessible to non-experts of the computational statistical community. The invention also relates to the practical applications of such a method, in particular in the biological field.

"The invention thus relates to a method of processing data for comparing at least two images using data processing means, the method comprising:

"extracting a first sample of coordinate values from at least one first image and a second sample of coordinate values from at least one second image;

"computing, with said processing means, an approximate normal score value based on a density test statistic function applied on the first and second samples of coordinate values, wherein said density test statistic function has an asymptotic distribution which can be normal or not;

"comparing a p-value, derived from the computed normal score value, with a predetermined level of significance in order to determine a similarity between the two images.

"Advantageously, the density test statistic function is a kernel density test statistic function.

"Advantageously, said density test statistic function is a multivariate kernel density test statistic function.

"In one preferred embodiment, the normal score value depends on the mean value of the density test statistic function. This normal score value can further depend both on the mean value and the variance value of the density test statistic function.

"Thus, in its embodiment, the method according to the present invention further comprises the following steps:

"selecting a first and a second optimal bandwidth matrices which are associated respectively with the first and second samples of coordinate values, wherein said bandwidth matrices are preferably a sequence of symmetric positive definite matrices; and

"determining the mean value estimator, and eventually the variance estimator, of the density test statistic function based on the selected optimal bandwidth matrices;

"wherein the density test statistic function is preferably based on a first estimator of a first integrated density functional associated with the first sample of coordinate values and a second estimator (of a second integrated density functional associated with the second sample coordinate values; and

"wherein said first and second bandwidth matrices are preferably selected to minimize the mean square error respectively of the first and second estimators in the space of all symmetric positive definite matrices.

"The invention further relates to a computer program product comprising code instructions for implementing the steps of a method of processing data according to the invention, when loaded and run on data processing means of an analyzing device.

"The invention also relates to a method for detecting a change between a first biological structure and a second biological structure, the method comprising the step of comparing an image of the first biological structure to an image of the second biological structure using the method according to the invention, wherein a change is detected when the image of the first biological structure and the image of the second biological structure are not found similar by said method according to the invention.

"The invention still relates to an analyzing device for detecting a change in a biological structure, the analyzing device comprising:

"image acquiring means able to capture at least one first image of a first element of said biological structure and at least one second image of a second element of this biological structure; and

"processing means configured to extract a first sample of coordinate values from the first image and a second sample of coordinate values from the second image, compute a normal score value based on a density test statistic function applied on the first and second samples of coordinate values, wherein the density test statistic function has an asymptotic distribution, and compare a p-value, derived from the computed normal score value, with a predetermined level of significance in order to determine a similarity between the first and second images.

"Further embodiments of the method of processing data, the computer program product, the method for detecting a change and the analyzing device according to the present invention are described in the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

"Other features and advantages of the invention will become apparent from the following description of non-limiting exemplary embodiments, with reference to the appended drawings, in which:

"FIG. 1 is a flow chart of a method of processing data for comparing images according to the present invention;

"FIG. 2 is a detailed flow chart of an embodiment of the normal score value computation step of method of processing data for comparing two images according to the present invention;

"FIG. 3A shows a flowchart illustrating a general method of determining whether a cellular condition is similar or not to another cellular condition, using the method of processing data for comparing images according to the present invention;

"FIG. 3B shows a flowchart of a method of determining the influence of a compound on a biological structure, which uses the method of determining whether a cellular condition is similar or not to another cellular condition according to the present invention;

"FIG. 3C illustrates a comparison of p-values obtained with the present invention and with the prior art method based on a resampling technique.

"FIG. 4 shows a flowchart of a method for detecting morphological changes over time of a biological structure, which uses the method of processing data for comparing images according to the present invention; and

"FIGS. 5-17 illustrate examples of detection of morphological changes in eukaryotic cells using the methods of processing data for comparing images and determining the influence of a compound on a biological structure according to the present invention"

URL and more information on this patent application, see: Schauer, Kristine; Duong, Thanh Buu; Goud, Bruno. Density-Based Method of Comparing Images and Detection of Morphological Changes Using the Method Thereof. Filed July 19, 2012 and posted June 19, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=3909&p=79&f=G&l=50&d=PG01&S1=20140612.PD.&OS=PD/20140612&RS=PD/20140612

Keywords for this news article include: Information Technology, Information and Data Processing, Centre National De La Recherche Scientifique Cnrs.

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Source: Information Technology Newsweekly


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