Particle Swarm Optimisation for Edge Detection in Noisy Images

<p>Detection of continuous and connected edges is very important in many applications, such as detecting oil slicks in remote sensing and detecting cancers in medical images. The detection of such edges is a hard problem particularly in noisy images and most edge detection algorithms suffer from producing broken and thick edges in such images. The main goal of this thesis is to reduce broken edges by proposing an optimisation model and a solution method in order to detect edges in noisy images. This thesis suggests a newapproach in the framework of particle swarm optimisation (PSO) to overcome noise and reduce broken edges through exploring a large area and extracting the global structure of the edges. A fitness function is developed based on the possibility score of a curve being fitted on an edge and the curvature cost of the curve with two constraints. Unlike traditional algorithms, the new method can detect edges with greater continuity in noisy images. Furthermore, a new truncation method within PSO is proposed to truncate the real values of particle positions to integers in order to increase the diversity of the particles. This thesis also proposes a local thresholding technique for the PSObased edge detection algorithm to overcome the problem of detection of edges in noisy images with illuminated areas. The local thresholding technique is proposed based on themain idea of the Sauvola-Pietkinenmethod which is a way of binarisation of illuminated images. It is observed that the new local thresholding can improve the performance of the PSO-based edge detectors in the illuminated noisy images. Since the performance of using static topologies in various applications and in various versions of PSO is different , the performance of six different static topologies (fully connected, ring, star, tree-based, von Neumann and toroidal topologies)within threewell-known versions of PSO (Canonical PSO, Bare Bones PSO and Fully Informed PSO) are also investigated in the PSO-based edge detector. It is found that different topologies have different effects on the accuracy of the PSO-based edge detector. This thesis also proposes a novel dynamic topology called spatial random meaningful topology (SRMT) which is an adoptation version of a gradually increasing directed neighbourhood (GIDN). The new dynamic topology uses spatial meaningful information to compute the neighbourhood probability of each particle to be a neighbour of other particles. It uses this probability to randomly select the neighbours of each particle at each iteration of PSO. The results show that the performance of the proposed method is higher than that of other topologies in noisy images in terms of the localisation accuracy of edge detection.</p>