Membrane algorithm improves image segmentation with artificial protozoa optimizer
MAAPO:an innovative membrane algorithm based on artificial protozoa optimizer for multilevel threshold image segmentation
Neural and Evolutionary Computing
Summary
Segmenting images means dividing an image into meaningful parts, but it's tricky to do well. This paper presents a new computer method called MAAPO that mimics tiny living organisms and uses a special membrane system to search for the best way to segment images. The membrane system helps try many ideas at once, making it more likely to find a good solution. The authors show MAAPO works better than other similar methods by testing it on common benchmark problems and real images.
What this means in practice
- •For computer vision engineers: Use MAAPO to improve automatic segmentation in applications requiring precise image analysis such as medical imaging or remote sensing.
- •For software developers for image processing: Integrate MAAPO as an advanced segmentation module to enhance the quality of processed images in photo editing and analysis tools.
Authors
Xiaopeng Wang, Vaclav Snasel, Seyedali Mirjalili, Jeng-Shyang Pan
Abstract
This paper proposes a novel membrane algorithm based on artificial protozoa optimizer (MAAPO) for global optimization problems. The artificial protozoa optimizer (APO) is adopted as the base meta-heuristic algorithm due to its novelty and competitive performance. MAAPO integrates two key innovations:(1) a membrane computing (MC) framework that introduces a parallel distributed paradigm to improve population diversity and search dynamics, and (2) an enhanced autotrophic model within APO that uses a roulette-based fitness-distance balance (RFDB) mechanism for adaptive reference point selection. These strategies collectively enhance the algorithm's exploration-exploitation balance and global search capabilities. To validate its performance, MAAPO is tested against 12 advanced algorithms on the CEC2017 test suite, and further applied to the multilevel thresholding image segmentation problem using Otsu and Kapur entropy as objective functions. The quality of segmented images is assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) metrics. Experimental results demonstrate that MAAPO outperforms its counterparts, delivering superior segmentation quality. This research on MAAPO contributes an effective enhancement strategy to meta-heuristic algorithms and introduces a novel, highly applicable approach for complex image segmentation tasks.