Gray level image segmentation with region growing method using modified ant lion optimization algorithm

Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. In this thesis, a modified version of the bio-inspired ant-lion optimization algorithm (mALO) is introduced to solve the region growing (RG) segmentation problem. The modification of the algorithm is done using a new balanced position update and flexible random walk boundary method. During the implementation, the median filter was applied to the input images to improve the quality of the images. Then, by finding the optimum seed points with the help of mALO, region growing segmentation was performed. The success of the proposed approach has been tested using images from the BSDS300 (Berkeley-300) dataset. In addition, the proposed algorithm was compared with the results of different algorithms in the literature. The results are presented with different comparison metrics such as J_e,d_max,d_min, DBI, XBI and Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index (FSIM), Boundary Displacement Error (BDE), Global Consistency Error (GCE), Correlation Coefficient (CC). Experimental results showed that the proposed method provided competitive results with those in the literature.