A method for imaging features on a microarray, comprising: - scanning the microarray and producing an image I thereof; - using an analysis device for overlaying a grid model on the image I; - using the analysis device for fitting the grid model to the locations of at least some of the features on the image I; and - using the analysis device for extracting images of the features, wherein the fitting comprises an iterative optimization of the result of an energy functional of the grid model, and wherein the result of the energy funtional is minimized, characterized in that said energy functional is defined as E P = αF P + βG P + γH P where α, β, and y are weighting factors, F is a distance term determined by the distance between adjacent points of the grid model, wherein the grid model comprises points connected by edges G is a perpendicularity term of the grid model, and H is a curvature term determined by the deviation of the points of the grid model from the locations of the features and computed from the curvature map of the image I, and the corresponding terms are given as F P = ∑ p i, j f ⁢ p i, j p i - 1, j + f ⁢ p i, j p i + 1, j + f ⁢ p i, j p i, j - 1 + f ⁢ p i, j p i, j + 1 with f p q = D interspot - d p q 2, G P = ∑ p i, j g p i, j ⁢ p i - 1, j → p i, j ⁢ p i, j - 1 → + g ⁢ p i, j ⁢ p i, j - 1 → p i, j ⁢ p i + 1, j → + g ⁢ p i, j ⁢ p i + 1, j → p i, j ⁢ p i, j + 1 → + g ⁢ p i, j ⁢ p i, j + 1 → p i, j ⁢ p i - 1, j → with g u → v → = u → • v → u → ⁢ v →, and H P = - ∑ p i, j curv I σ p i, j, wherein p i,j are the points of the grid model, D interspot is the initial value of the distance and d(p, q) represents the current distance of two points p and q connected by an edge, the curvature map curv() is defined as the product of the Gaussian curvature of image I σ with the image I σ point by point, and I σ is the result of filtering the image I with a Gaussian Kernel of size σ. - scanning the microarray and producing an image I thereof; - using an analysis device for overlaying a grid model on the image I; - using the analysis device for fitting the grid model to the locations of at least some of the features on the image I; and - using the analysis device for extracting images of the features, wherein the fitting comprises an iterative optimization of the result of an energy functional of the grid model, and wherein the result of the energy funtional is minimized, characterized in that said energy functional is defined as E P = αF P + βG P + γH P where α, β, and y are weighting factors, F is a distance term determined by the distance between adjacent points of the grid model, wherein the grid model comprises points connected by edges G is a perpendicularity term of the grid model, and H is a curvature term determined by the deviation of the points of the grid model from the locations of the features and computed from the curvature map of the image I, and the corresponding terms are given as F P = ∑ p i, j f ⁢ p i, j p i - 1, j + f ⁢ p i, j p i + 1, j + f ⁢ p i, j p i, j - 1 + f ⁢ p i, j p i, j + 1 with f p q = D interspot - d p q 2, G P = ∑ p i, j g p i, j ⁢ p i - 1, j → p i, j ⁢ p i, j - 1 → + g ⁢ p i, j ⁢ p i, j - 1 → p i, j ⁢ p i + 1, j → + g ⁢ p i, j ⁢ p i + 1, j → p i, j ⁢ p i, j + 1 → + g ⁢ p i, j ⁢ p i, j + 1 → p i, j ⁢ p i - 1, j → with g u → v → = u → • v → u → ⁢ v →, and H P = - ∑ p i, j curv I σ p i, j, wherein p i,j are the points of the grid model, D interspot is the initial value of the distance and d(p, q) represents the current distance of two points p and q connected by an edge, the curvature map curv() is defined as the product of the Gaussian curvature of image I σ with the image I σ point by point, and I σ is the result of filtering the image I with a Gaussian Kernel of size σ.