It is well known that edge is the basic feature of image and the important property for image analysis and recognition. Using traditional edge extraction

strong edge can be efficiently extracted but detail edge information may be lost

however

these detail edge information are often important features in some real applications. This paper proposes a new method to extract image detail edge based on the combination of gray-morphology and image decomposition. First gray-morphological operators are used to detect edge image and remove part background and noise

then it is decomposed into several areas using quad-tree method

continuous decomposition is terminated when the area size is equal or smaller than the minimal area size parameter

finally

different thresholds for different areas are selected to ensure the integrality of edge extraction

in order to void smooth background are involved and some detail edges are lost

global minimal and maximal thresholds are set beforehand to limit the scope of selected threshold

when area threshold is smaller than global minimal threshold

it will be replaced by the global minimal threshold

inversely

by global maximal threshold. Simulations show that this method can efficiently extract detail edges from both noiseless images and noise images.