Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation - Alia, Osama Moh’d Radi (2011)

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Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset. This thesis aims to solve these problems using an efficient metaheuristic algorithm, known as the Harmony Search (HS) algorithm. First, two alternative HS-based fuzzy clustering methods are proposed. The aim of these methods is to overcome the limitation faced by conventional fuzzy clustering algorithms, which are known to provide sub-optimal clustering depending on the choice of the initial clusters. Second, a new dynamic HS-based fuzzy clustering algorithm (DCHS) is proposed to automatically estimate the appropriate number of clusters as well as a good fuzzy partitioning of the given dataset. These algorithms have been applied to the problem of image segmentation. Various images from different application domains, including synthetic and real-world images, have been used in this thesis to show the applicability of the proposed algorithms. Finally, the proposed DCHS algorithm is applied to two real-world medical image problems, namely, malignant bone tumour (osteosarcoma) and magnetic resonance imaging brain segmentation. The experimental results are very promising showing significant improvements compared to other approaches in the same domain.