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

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Abstract

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.

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Acknowledgements

Many thanks to the Deanship of Scientific Research at Imam Abdulrahman Bin Faisal University. This research was funded by Imam Abdulrahman Bin Faisal University, with a grant titled “Medical Image Segmentation using Unsupervised Classification based Swarm Intelligence Algorithms for Cancer Detection and Extraction” No. 2020-064-PYSS, Date 25/4/2020.

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Authors and Affiliations

  1. School of Information Technology, Skyline University College, Sharjah, P.O. Box 1797, United Arab Emirates

    Waleed Alomoush & Ayat Alrosan

  2. Department of Management Information Systems, College of Applied Studies and Community Services, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    Yazan M. Alomari

  3. IBM Center of Excellence, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300, Kuantan, Pahang, Malaysia

    Alaa A. Alomoush & Hammoudeh S. Alamri

  4. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Ammar Almomani

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  1. Waleed Alomoush
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  4. Alaa A. Alomoush
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Correspondence to Waleed Alomoush.

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Alomoush, W., Alrosan, A., Alomari, Y.M. et al. Fully automatic grayscale image segmentation based fuzzy C-means with firefly mate algorithm. J Ambient Intell Human Comput 13, 4519–4541 (2022). https://doi.org/10.1007/s12652-021-03430-3

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