Abstract: The present invention provides to image processing methods that include a method of selecting an optimal threshold value (to) for an image comprising the steps of: obtaining an image; selecting a test segment of the said image; determining the mean feature size (S) of features appearing in the test segment at each of a plurality of threshold values (t), so as to produce mean feature size data (S(t)); selecting a relevant subset of the mean feature size data (S(t)); and determining an optimal threshold value (to) as a function of said subset of the mean feature size data. The present invention additionally provides methods of thresholding an image to produce a binary image by application of the optimal threshold value (to) determined according to the methods of the present invention.

Abstract: A method and apparatus for identifying the orientation and positions of the elements of a grid in an image. The elements of the grid need not be parallel to the sides of the image, and the image need not contain all of the elements of the grid. The method can be applied to multi-sided grids, whether rectilinear or distorted. The addresses of identified grid elements can be used to control, e.g., analytical procedures directed to individual elements.

Abstract: A method and apparatus for identifying the orientation and positions of the elements of a grid in an image. The elements of the grid need not be parallel to the sides of the image, and the image need not contain all of the elements of the grid. The method can be applied to rectilinear and curved grids. The addresses of identified grid elements can be used to control, e.g., analytical procedures directed to individual elements.

Abstract: The present invention provides to image processing methods that include a method of selecting an optimal threshold value (tO) for an image comprising the steps of: obtaining an image; selecting a test segment of the said image; determining the mean feature size (S) of features appearing in the test segment at each of a plurality of threshold values (t), so as to produce mean feature size data (S(t)); selecting a relevant subset of the mean feature size data (S(t)); and determining an optimal threshold value (tO) as a function of said subset of the mean feature size data. The present invention additionally provides methods of thresholding an image to produce a binary image by application of the optimal threshold value (tO) determined according to the methods of the present invention.

Abstract: Patterns in an image or graphical representation of a dataset are identified through centroid integration. The image or graphical representation is digitized. A collapsed image is created by identifying the centroid and at least one characteristic value for each feature in the digitized image. A shape, such as a line, curve, plane or hypersurface, is stepped across the image. At each step, the characteristic values are summed for all centroids within a predetermined distance of the shape. Peaks in the resulting summation represent matches between the shape and a pattern in the data, with steeper peaks representing a better match. Different shapes, or the same shape at different angles, can be applied to the data to find better fits with the patterns in the image. The image can be multidimensional, with the shape being one dimension less than the image.