34 results about "Microarray image" patented technology

A method and system are disclosed for automatically creating crosstalk-corrected data of a microarray utilizing calibration dye spots each of which comprises a single pure dye. A microarray scanner, such as a confocal laser microarray scanner, generates dye images, each of which contains at least one of the calibration dye spots for each of the output channels of the scanner. For each of the calibration dye spots, an output of each of the output channels is measured to obtain output measurements. A set of correction factors is computed from the output measurements to correct the data subsequently gathered from the microarray scanner. In other words, the correction factors are applied to quantitation or measurement data obtained from microarray images which contain spots having dyes of known or unknown excitation or emission spectra to obtain crosstalk-corrected data.

A method and system for automated quantitation of tissue micro-array image (TMA) digital analysis. The method and system automatically analyze a digital image of a TMA with plural TMA cores created using a needle to biopsy or other techniques to create standard histologic sections and placing the resulting needle cores into TMA. The automated analysis allows a medical conclusion such as a medical diagnosis or medical prognosis (e.g., for a human cancer) to be automatically determined. The method and system provides reliable automatic TMA core gridding and automated TMA core boundary detection including detection of overlapping or touching TMA cores on a grid.

A method for determining relative incidence of a binding substance within two biological samples is provided. The two samples are labeled with luminescent materials having different chromatic properties. An image of the luminescent materials upon a binding site of a microarray is analyzed as two clusters of data points scattered about respective representative pairs of chromatic intensity values. The relative incidence of the binding substance is determined as a ratio of differences between corresponding indices of the representative pairs.

Technology is disclosed for highly accurate automated execution of processing for alignment of a detection area to a DNA microarray image file and processing for quantitative determination of success/failure of the alignment during DNA microarray analysis. A probe reactive chip used for the technology comprises a substrate; a spot area wherein spots for fixing a probe capable of specifically reacting to a sample marked so as to be optically detectable are formed in a matrix on a surface of the substrate; and a reference pattern area, which is arranged within the spot area or approximate to the spot area, and comprises a plurality of different alignment marks in order to correct misalignment of the spot during analysis of the sample on the surface of the substrate.

A microarray image is segmented into discreet segments for respective spots in the microarray image based on locating microarray image rows and columns by maximizing scores associated with vertical and horizontal image projections. To determine the locations of the rows and columns that extend through artifacts that involve multiple spots and/or multiple columns and rows, the scores may also include contributions from the gaps between adjacent rows and columns. The scores are calculated by stepping windows containing nominally spaced spot-sized row or column boundaries, over the horizontal projection curves. At each step a total row or column score is calculated that represents the areas of the projection curves that fall within the row boundaries. The system then selects the window positions that correspond to the maximum total row score and total column score, and uses the locations of the boundaries as the locations of row and column “stripes” that are sized to the spot diameters. The intersections of the row and column stripes define the segments for the respective spots, and the centers of the intersections are the locations for the spot location, or grid, markers. The system may also determine the best spot size, column and row gap sizes and sub-array gap sizes by changing the boundaries accordingly and determining corresponding maximum scores.

A remote microarray analysis system, method and apparatus for use in the remote analysis of a chemical compound microarray supported on a substrate is disclosed. Pixel image data is received from a remote location including image data that depicts (a) a calibration scale associated with the substrate and (b) the microarray. A transformation action of said pixel data corresponding to the calibration scale is determined and the received image data corresponding to at least the microarray is adjusted by applying the transformation action. The adjusted image of the microarray is compared with a database of stored microarray pixel data to extract information from said image.

Microarrays are imaged by an illumination and detection system that supplies excitation light through one or more optical fibers, each fiber transmitting excitation light from an excitation light source to a single spot in the microarray. Emission light from each spot is then collected by a collimating lens and converted to a signal that is compiled by conventional software into an image of the entire microarray. The optical fiber(s) and the collimating lens are arranged such that the direction of travel of the excitation light and the direction along which the emission light is collected are not coaxial, and preferably both are at an acute angle to the axis normal to the plane of the microarray.

A microarray processing system provides to a user an ability to draw one or more contour lines around portions of the microarray considered by the user to be undamaged, non-defective, and otherwise not compromised and therefore suitable for feature extraction. The microarray processing system then constructs one or more rectangular regions of feature extractability based on the user-indicated subregions of feature extractability, and proceeds to extract data from the one or more rectangular regions of feature extractability.

The invention relates to the technical field of digital image processing, and discloses a fluorescence intensity detection method and device for a porous fluorescent microarray image, computer equipment and a computer readable storage medium. After a porous fluorescent microarray image is subjected to filtering and denoising processing, image binarization processing and morphological processing in sequence, a binarized image which is easy to perform contour extraction accurately can be obtained, and for a boundary contour extracted from the binarized image, determination of a bounding box, gray value traversal calculation of pixel points in the box and final background removal calculation of an average gray value are performed in sequence, so that the algorithm operation speed can be improved, the accuracy of the finally obtained fluorescence intensity detection result can be ensured, the fluorescence intensity data of each single-hole fluorescence region object can be quickly and accurately obtained, the purposes of real-time detection and quantitative analysis can be realized, and the method is convenient for practical application and popularization.

The invention provides a prostate cancer tissue microarray grading method based on a convolutional neural network. The method comprises the following steps: acquiring prostate cancer tissue microarrayimage data; preprocessing the prostate cancer tissue microarray image data; establishing an image segmentation model based on the preprocessed prostate cancer tissue microarray image data, and inputting the prostate cancer tissue microarray image data into the image segmentation model; restoring an output result of the image segmentation model in the size the same with that of the original image;and performing consistency check and comparison on the prediction result of the image segmentation model and the expert labeling result, and outputting a comparison result. According to the method, the characteristics of the deep layer and the shallow layer can be fused through the multi-scale self-attention network, meanwhile, the characteristics of each scale are supervised, network parameterscan be reduced, the calculation efficiency can be improved, and the effectiveness of the method is verified on a completely marked data set.

The present invention relates to digital pathology. In order to facilitate analyzing a tissue microarray, an apparatus is provided for tissue examination. The apparatus comprises a data input (102), a tissue microarray analyzing unit (104), and an output (106). The data input is configured to receive a reference image of a reference slice obtained from a tissue sample block; and to receive a microarray image of a microarray slice comprising at least one tissue core obtained from at least the tissue sample block. The tissue microarray analyzing unit is configured to register tissue core images of at least one tissue core with the reference image based on a spatial arrangement of the respective tissue core within the tissue sample block. The output is configured to provide a registered result obtained from the tissue microarray analyzing unit for further analyzing purposes.

Scanning of a microarray is performed through a mask that exposes a plurality, but not all, of the sites of the microarray, and either the mask is movable relative to the microarray or the microarray is movable relative to the mask, or both. The mask is useful as a means of restricting the illumination of sites on the microarray to those that can be illuminated while the scan head is traveling at a steady, target velocity, blocking the passage of light between the scan head and the microarray at those points in the scan head trajectory where the scan head is either accelerating or decelerating. The mask is also useful for reducing background noise in the microarray image by preventing light spillage to sites adjacent to those being scanned.

Expression profiling using DNA microarrays is an important new method for analyzing cellular physiology. In “spotted” microarrays, fluorescently labeled cDNA from experimental and control cells is hybridized to arrayed target DNA and the arrays imaged at two or more wavelengths. Statistical analysis is performed on microarray images and show that non-additive background, high intensity fluctuations across spots, and fabrication artifacts interfere with the accurate determination of intensity information. The probability density distributions generated by pixel-by-pixel analysis of images can be used to measure the precision with which spot intensities are determined. Simple weighting schemes based on these probability distributions are effective in improving significantly the quality of microarray data as it accumulates in a multi-experiment database. Error estimates from image-based metrics should be one component in an explicitly probabilistic scheme for the analysis of DNA microarray data.

In a method for providing rapid and simple manually driven alignment of image segmentation grids to images of imperfect mircroarrays, an image of a microarray composed of multiple sub-arrays or blocks is displayed and a nominal grid composed of corresponding sub-arrays or blocks is superimposed on that image. Comer markers of a grid block are dragged to coincide with the spots at the corresponding corners of the underlying image block. The locations of the intervening grid markers in that block are automatically adjusted by linear interpolation in two dimensions. The corrections generated for this grid block are then applied automatically to all of the other blocks in the grid. Following this, the corner grid blocks are dragged to align a single corner marker within each corner grid block with an image spot at the corresponding corner block of the image, and all of the intervening grid blocks are automatically aligned to these by linear interpolation in two dimensions.

A method of automatically identifying the microarray chip corners and probes, even if there are no probes at the corners, in a high density and high resolution microarray 5 scanned image having an image space, wherein the method minimizes the error distortions in the image arising in the scanning process by applying to the image a multipass corner finding algorithm comprising: (a) applying a Radon transform to an input microarray image to project the image into an angle and distance space where it is possible to find the orientation of the straight lines; (b) applying a fast Fourier transform to the projected image 10 of (a) to find the optimal tilting angle of the projected image; (c) determining the optimal first and last local maxima for the optimal tilting angle; (d) back projecting the determined first and last local maxima to the image space to find the first approximation of the first and last column lines of the image; (e) rotating the image and repeating steps (a) through (d) to find the first approximation of the top and bottom row lines of the image; (f) determining 15 the first approximation of the four corners of the image from the intersection of the column and row lines; (g) applying a heuristic for determining if the first approximation of step (f) is sufficient; and (h) optionally trimming the scanned image around the first approximation of the four corners and repeating steps (a) through (f).

In a microarray image analysis system, when one of a plurality of statuses is set for a spot of a microarray by the user, the status of a similar spot is automatically determined. In a microarray image, the user determines a status of a spot, the pixel value matrix of an image in a spot region is learned by a neural network, a vertically and horizontally symmetrical image and an image rotated about the center of the region are formed and are learned by the neural network, and the neural network formed by repeating these steps is used for automatically recognizing the status of an undecided spot.