1. A method for electronically processing an electronically stored image by a computer having a processor, circuitry for controlling the processor and memory for simultaneously extracting edges of all lengths, whether the image is a natural or a noisy image, and for enabling visualization of the edges, by one of displaying the resulting image on a monitor, storing such images in a memory for future visualization, developing copies of images, and printing a copy of the resulting image comprising the steps of: a. storing an image in O (N log ρ), where N is the number of pixels in the image, and ρ is the length of the longest edge structure of interest pixel by pixel in the memory of the computer; b. storing non-transitory computer coded instructions for configuring computer circuitry for programming the processor of the computer for; i. filtering the stored image to obtain a set of responses that measures difference of oriented means of various lengths and orientations, and to construct all significantly different oriented means at all locations, lengths, and orientations in the image; ii. said filtering being carried out by a multiscale edge detection algorithm based on I(x, y) denoting a continuous function representing image intensities given in a two-dimensional domain and the oriented means comprising the family of averages of I(x, y) being denoted along rectangular patches of a given center location x=(x, y), length L, width w, and orientation θ with w set to a small constant yielding elongated oriented means; iii. processing the oriented means via integrals of the form F ⁡ ( x , L , w , θ ) = 1 wL ⁢ ∫ - w / 2 w / 2 ⁢ ∫ - L / 2 L / 2 ⁢ I ⁡ ( x + γcos ⁢ ⁢ θ - δsinθ , y + γsinθ + δcosθ ) ⁢ ⁢ ⅆ γ ⁢ ⁢ ⅆ δ. ( 1 ) to obtain a family of responses of differences of oriented means defined as D ⁡ ( x , L , w , s , θ ) = F ⁡ ( x + s 2 ⁢ ( - sin ⁢ ⁢ θ , cos ⁢ ⁢ θ ) , L , w 2 , θ ) - F ⁡ ( x - s 2 ⁢ ( - sin ⁢ ⁢ θ , cos ⁢ ⁢ θ ) , L , w 2 , θ ) , ( 2 ) with s≧w/4 to avoid overlap, and wherein D defines a difference of neighboring orientations of the same orientation; iv. determining the significance of a collection of such responses by applying a scale adaptive threshold algorithm t ⁡ ( L , w , N ) = σ ⁢ 2 ⁢ ⁢ ln ⁢ ⁢ N wL , ( 6 ) to the collection of responses wherein pixel noise is determined as N(0,σ 2 ), and wherein σ is a parameter of pixel noise, and wherein a response which exceeds t(L, w, N) can potentially indicate the existence of a real edge; v. implementing a recursive decision step to classify the collection of responses according to the following classes a. a length-L response D is classified significant if it exceeds the respective threshold, D>T L , and its total gap size is below βL and the response is marked as a potential edge and its gap size is reset to zero; b. a length-L response D is classified nearly-significant if αT L <D≦T L and its total gap size is below βL and its gap size is not updated; c. a length-L response D is classified non-significant if D≦αT L or if its total gap size exceeds βL and its total gap size is reset to L; wherein α and β are preselected parameters (0≦α,β≦1) and wherein αT L is set to be a low threshold for length-L responses, namely, responses of length L that exceed αT L and do not exceed T L and are considered nearly significant, and βL is set to be a threshold on the total gap size for length-L edges, i.e., a length-L edge is not be considered significant if it contains sub-portions of total length>βL that do not exceed their respective low threshold; vi. wherein the classification of the collection of responses D(x, L, w, s, θ) obtained for filters of some predetermined short length, e.g., L=1 is carried out and each response is classified to one of the following three classes: (1) significant (D>T L ) (2) nearly-significant (αT L <D≦T L ) and (3) non-significant (D≦αT L ) and for the first two classes, the total gap size is set to “0”, while for the latter class the gap size is set to “L”, and proceeding sequentially considering lengths L of powers of 2; vii. testing identified potential edges for statistical significance by examining the intensity profiles along the two sides of a potential edge and eliminating potential edges that do not meet a given standard and marking the remaining edges as significant edges; viii. applying angular non-maximal suppression to each remaining edge to ensure well-localized significant edges; ix. applying spatial inter-scale suppression to ensure that each significant edge is identified with its maximal perceptual length; x. outputting data regarding significant edges of the image to memory; and xi. enabling visualization of the edges, by one of displaying the resulting image on a monitor, storing such images in a memory for future visualization, developing copies of images, and printing a copy of the resulting image.