Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function

An improved particle swarm optimization for multilevel thresholding medical image segmentation

Multilevel thresholding image segmentation is one of the widely used image segmentation methods, and it is also an important means of medical image preprocessing. Replacing the original costly exhaustive search approach, swarm intelligent optimization algorithms are recently used to determine the optimal thresholds for medical image, and medical images tend to have higher bit depth. Aiming at the drawbacks of premature convergence of existing optimization algorithms for high-bit depth image segmentation, this paper presents a pyramid particle swarm optimization based on complementary inertia weights (CIWP-PSO), and the Kapur entropy is employed as the optimization objective. Firstly, according to the fitness value, the particle swarm is divided into three-layer structure. To accommodate the larger search range caused by higher bit depth, the particles in the layer with the worst fitness value are employed random opposition learning strategy. Secondly, a pair of complementary inertia weights are introduced to balance the capability of exploitation and exploration. In the part of experiments, this paper used nine high-bit depth benchmark images to test the CIWP-PSO effectiveness. Then, a group of Brain Magnetic Resonance Imaging (MRI) images with 12-bit depth are utilized to validate the advantages of CIWP-PSO compared with other segmentation algorithms based on other optimization algorithms. According to the segmentation experimental results, thresholds optimized by CIWP-PSO could achieve higher Kapur entropy, and the multi-level thresholding segmentation algorithm based on CIWP-PSO outperforms the similar algorithms in high-bit depth image segmentation. Besides, we used image segmentation quality metrics to evaluate the impact of different segmentation algorithms on images, and the experimental results show that the MRI images segmented by the CIWP-PSO has achieved the best fitness value more times than images segmented by other comparison algorithm in terms of Structured Similarity Index and Feature Similarity Index, which explains that the images segmented by CIWP-PSO has higher image quality.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue.

Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.

Ultra-widefield and high-speed spiral laser scanning OR-PAM: System development and characterization

Photoacoustic microscopy (PAM) is a high-resolution imaging modality that has been mainly implemented with small field of view applications. Here, we developed a fast PAM system that utilizes a unique spiral laser scanning mechanism and a wide acoustic detection unit. The developed system can image an area of 12.5 cm² in 6.4 s. The system has been characterized using highly detailed phantoms. Finally, the imaging capabilities of the system were further demonstrated by imaging a sheep brain ex vivo and a rat brain in vivo.