Fully automatic grayscale image segmentation based fuzzy C-means with firefly mate algorithm

Image segmentation is the method of dividing an image into many segments, comprising groups of pixels. It is a process used to determine objects within the image. Fuzzy c-means (FCM) technique has been popularly employed as grayscale image segmentation method. Meanwhile, the conventional FCM suffers from some drawbacks including easy fall into local optimal solution resulting from inappropriate selection of the initial cluster center values and optimal number of clusters (regions) for each image without a prior knowledge or input by the operator. To solve FCM issues, the paper proposes a new fully automatic segmentation method for grayscale images based on fuzzy c-means with firefly mate algorithm (AUTO-FCM-FMA). This approach utilizes the mate list (M) mechanism with firefly algorithm (FMA) to search for the near-optimal number clusters, the location of centroids by exploring the search space and void stuck in local optimum, and the best outcomes from FMA as input for FCM. To evaluate its effectiveness, the proposed algorithm was tested on different types of images. These images can be categorized into simulated MRI images (normal and MSL), synthetic images and natural images. All these images cover different domains and levels of difficulty (e.g. clusters overlapping). The results of validation experiments were encouraging, especially when the performance of proposed algorithm outcomes was compared to that of other state-of-the-art algorithms.