Article
Computer Science, Artificial Intelligence
Dong Wang, Xiao-Ping Wang
Summary: In this paper, a novel iterative convolution-thresholding method (ICTM) is proposed for image segmentation models. It utilizes the characteristic functions of domains to represent the interface between different segment domains and approximates the fidelity term and regularization term using linear functional and heat kernel convolution, respectively. The method achieves the decaying energy property and provides unconditional stability.
PATTERN RECOGNITION
(2022)
Article
Biology
Khalid M. Hosny, Asmaa M. Khalid, Hanaa M. Hamza, Seyedali Mirjalili
Summary: Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems. This study proposed a multilevel thresholding technique that improved upon existing algorithms by hybridizing the Coronavirus Optimization Algorithm and the Harris Hawks Optimization Algorithm. The proposed algorithm demonstrated superior performance in terms of convergence to the global optimum and the quality of segmented images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Neurosciences
Mandong Hu, Yi Zhong, Shuxuan Xie, Haibin Lv, Zhihan Lv
Summary: The study improved the fuzzy clustering algorithm and designed a brain image processing and brain disease diagnosis prediction model based on fuzzy clustering and HPU-Net. Experimental results show that the improved algorithm has more nodes, lower energy consumption, and greater stability compared to other models under the same conditions.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Computer Science, Information Systems
Shubham Mahajan, Nitin Mittal, Amit Kant Pandit
Summary: This paper proposes a novel image thresholding technique based on Adaptive Flower Pollination Algorithm and type II fuzzy entropy. Through the evaluation of quality, convergence and accuracy, the effectiveness of this technique in image segmentation is demonstrated.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz, Songfeng Lu, Sibo He
Summary: This paper presents a multilevel thresholding image segmentation method based on enhancing the performance of the whale optimization algorithm (WOA), called the multi-leader whale optimization algorithm (MLWOA). MLWOA integrates different tools with WOA to improve exploration ability and avoid the trap of local optima during the search process.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Khalid M. Hosny, Asmaa M. Khalid, Hanaa M. Hamza, Seyedali Mirjalili
Summary: This paper presents a modified Coronavirus Optimization algorithm for image segmentation. The algorithm increases the diversity of solutions by incorporating the concept of chaotic maps in the initialization step. A hybrid of two commonly used methods is applied as the fitness function to determine the optimal threshold values. Evaluation using different datasets demonstrates the superiority of the proposed algorithm in image segmentation.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
R. Premalatha, P. Dhanalakshmi
Summary: The current research explores object enhancement and segmentation for CT images of lungs infected with COVID-19 using Pythagorean fuzzy entropy, measures, and thresholding technique. The proposed scheme shows the best effect on object separation and quality measurement values compared to other segmentation algorithms in terms of object extraction ability.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Simrandeep Singh, Nitin Mittal, Harbinder Singh
Summary: This paper introduces a new hybrid Dragonfly algorithm and Firefly Algorithm for image segmentation, showing that the proposed method outperforms other optimization algorithms such as MTEMO, GA, PSO, and BF in terms of performance metrics.
Article
Computer Science, Artificial Intelligence
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Huiyu Zhou, Mahshid Helali Moghadam
Summary: Multi-level image thresholding is a widely used method for image segmentation, but it can be time-consuming due to the need for an exhaustive search to find optimal threshold values. In this paper, we evaluate 23 population-based metaheuristics for multi-level image thresholding and compare their performance using various measures. Our experimental results show that recently introduced algorithms may not perform well in this task, while some established algorithms work better.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Omar A. Kittaneh
Summary: This paper proposes a new multi-level entropy-based image thresholding method that relies on the minimum of the variance entropy. The method is fully automated and produces segmentation results comparable to the generalized Otsu's method, which requires human intervention. It also outperforms the generalized Kapur's method in benchmarking entropy-based thresholding techniques. The method is successfully applied to various scenarios and its performance is checked using classification measures and quality metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mohammad Al-Azawi, Yingjie Yang, Howell Istance
Summary: This paper introduces a saliency extraction technique inspired by the human visual system, which identifies salient regions in a scene through local saliency identification and global saliency identification stages. The proposed method has several advantages over existing methods and demonstrates high efficiency in testing.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Analytical
Wysterlanya K. P. Barros, Leonardo A. Dias, Marcelo A. C. Fernandes
Summary: This study implements the Otsu automatic image thresholding algorithm on FPGA for real-time processing of high-resolution images. By optimizing processing time through parallelization, the proposed hardware achieved a high speedup compared to similar works.
Article
Computer Science, Artificial Intelligence
Francesco Bardozzo, Borja De La Osa, Lubomira Horanska, Javier Fumanal-Idocin, Mattia delli Priscoli, Luigi Troiano, Roberto Tagliaferri, Javier Fernandez, Humberto Bustince
Summary: This paper introduces a new adaptive binarization technique FLAT based on fuzzy integral images, as well as new generalizations of different fuzzy integrals and a modified design of SAT. Experimental results demonstrate that the proposed methodology produces better thresholds than other global and local thresholding algorithms.
INFORMATION FUSION
(2021)
Article
Mathematics
Jorge Munoz-Minjares, Osbaldo Vite-Chavez, Jorge Flores-Troncoso, Jorge M. Cruz-Duarte
Summary: This paper proposes a strategy for object segmentation based on CSA and GG distribution, and validates its advantages in both synthetic and practical scenarios through experiments. The results show that this strategy outperforms other algorithms in simulated environments and ranks among the best algorithms in real-world scenarios.
Article
Computer Science, Artificial Intelligence
Jiangxiong Fang, Huaxiang Liu, Jun Liu, Haiying Zhou, Liting Zhang, Hesheng Liu
Summary: This paper introduces a novel global and local fuzzy image fitting (GLFIF) based active contour model for image segmentation. By designing global and local fitted images and constructing an energy function, it addresses intensity inhomogeneity in images and proves convexity to ensure segmentation independence of initialization. Experimental results demonstrate the model's robustness in segmenting images.
APPLIED SOFT COMPUTING
(2021)
