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Using integral transforms to the end of lines detection in images withcomplex background, makes the detection a hard task needing additionalprocessing to manage the detection. As an integral transform, the Scale SpaceRadon Transform (SSRT) suffers from such drawbacks, even with its greatabilities for thick lines detection. In this work, we propose a method toaddress this issue for automatic detection of thick linear structures in grayscale and binary images using the SSRT, whatever the image background content.This method involves the calculated Hessian orientations of the investigatedimage while computing its SSRT, in such a way that linear structures areemphasized in the SSRT space. As a consequence, the subsequent maxima detectionin the SSRT space is done on a modified transform space freed from unwantedparts and, consequently, from irrelevant peaks that usually drown the peaksrepresenting lines. Besides, highlighting the linear structure in the SSRTspace permitting, thus, to efficiently detect lines of different thickness insynthetic and real images, the experiments show also the method robustnessagainst noise and complex background.

Guided Scale Space Radon Transform for linear structures detection