Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds

A Fast Texture Synthesis Technique using Spatial Neighborhood

In this technical report, we present a texture synthesis technique that stores all possible histories in memory structure acting as a Finite State Machine. The histories, which are the most probable spatial neighborhood for each pixel or patch, are stored in each state of the FSM. After constructing that structure, the synthesis goes very fast and smooth in a raster scan order. We found that our technique works well for a wide variety of textures, ranging from stochastic to regular structured textures, including near-regular textures. We show that our technique is faster, and gives better results than previous pixel- or patch-based techniques, with minimal user input. Keywords: keyword1, Texture synthesis, Image processing, Pixel-based textures, Markov random field, and Image-based rendering

Feature-Based Texture Synthesis using Voronoi Diagrams Technical

In this technical report, we present a technique to synthesize featured textures easily and interactively. The main idea is to synthesize the texture by copying irregular patches from the source to the target texture, each of them containing a complete feature. The interior part of the feature is not touched while the cutting and stitching is done on the background texture between the features. The technique starts by selecting a feature in the source texture by the user, after which the algorithm finds the positions of other features, generates a similar distribution of features, and finally synthesises the target texture by copying and stitching patches of the target’s Voronoi cellular shapes from the source texture