Quantum mechanics

Context (archaeology)

Otsu's method

Entropy (arrow of time)

Image processing

Archaeology

Selection (genetic algorithm)

Computer science

Thresholding

Scale-space segmentation

Image segmentation

Physics

Segmentation

Pattern recognition (psychology)

Geography

Image (mathematics)

Artificial intelligence

Computer vision

Measurement

Medical image processing is one of the real research regions in the most recent four decades. Numerous researchers have contributed very great algorithms and reported outstanding results. In this paper, adaptive wind driven optimization (AWDO) based multilevel thresholding is implemented for the segmentation Magnetic Resonance Image (MRI) brain images. The axial T2-weighted MRI brain images are considered for image segmentation. The effectiveness of the AWDO for multilevel thresholding of MR images is yet to be explored, and this paper presents humble contribution in this context. The optimal multilevel thresholding is found by maximizing the very popular objectives such as between class variance (Otsu method) and Kapur’s entropy. The efficiency of proposed approach was compared with the outcomes of existing algorithms like RGA, GA, Nelder–Mead simplex, PSO, BF and ABF. To check the effectiveness of the proposed algorithm, experimental results are analyzed quantitatively and qualitatively. The results showed the superiority of the proposed approach regarding better segmentation results.

Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization