Image segmentation framework based on optimal multi-method fusion

This study presents a multi-method fusion and optimisation framework that can optimally combine different existing methods to further enhance the segmentation performance. The framework, in which the original accumulating process is improved and a new combination process is added, is the extension of the previously developed `accumulated local fuzzy c-means with spatial information' method. In the improved accumulating process, different segmentation methods are utilised in local windows to judge whether each pixel belongs to the object. In the new combination process, the accumulated results of different segmentation methods are weighted combined, where the weights of different methods are optimised by the genetic algorithm with the objective of minimising standard deviations of both the object and the background pixels. Typical images and all images in the Weizmann's Segmentation Evaluation Database are tested in the experiments. The results show that the authors' method can perform better than some state-of-the-art methods, and combining more methods in the framework can bring better performance. Moreover, the proposed multi-method combination framework is parameterless, which increases its adaptability in various applications.