Multiple linear feature detection based on multiple-structuring-element center-surround top-hat transform

Abstract

Linear feature detection is an important technique in different applications of image processing. To detect linear features in different types of images, a simple but effective algorithm based on a multiple-structuring-element center-surround top-hat transform is proposed. The center-surround top-hat transform is discussed and analyzed. Based on the properties of this transform for image feature detection, multiple structuring elements are constructed corresponding to the possible linear features at different directions. The whole algorithm is divided into four parts. First, the algorithm uses the center-surround top-hat transform to detect all the possible linear features at different directions through constructing multiple structuring elements. Second, the detected linear feature regions at each direction are processed by a closing operation to remove the possible holes or unconnected regions. Third, the processed results of the detected linear feature regions at all directions are combined to form all the possible detected linear feature regions. Fourth, the combined result is refined by using some simple operations to form the final result. Experimental results on different types of images from different applications verified the effective performance of the proposed algorithm. Moreover, the experimental results indicate that the proposed algorithm could be used in different applications.