A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Qin, Jun; Wang, Chuting; Qin, Guanshi

doi:10.1155/2019/6706590

Cited by 11 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They concluded that compared to traditional methods, the QPSO improved both accuracy and speed. Qin et al [60] employed the subspace elimination optimization (SSEO) for MLT image segmentation. They applied the SSEO for four different images, and they compared it to the particle swarm optimization (PSO).…”

Section: Related Workmentioning

confidence: 99%

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

et al. 2020

View full text Add to dashboard Cite

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and mothflame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Although numerous effective threshold segmentation algorithms are in use, traditional methods may struggle to meet the requirements of more detailed and precise segmentation, making multidimensional threshold segmentation more suitable for efficient targeting of images. In 2019, Qin et al proposed a multi-level thresholding method for images based on subspace elimination optimization [4]. Zhao et al introduced a chaos-randomized ant colony optimization approach, employing two-dimensional maximum entropy for multi-threshold image segmentation [5].…”

Section: Introductionmentioning

confidence: 99%

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

Li,

Zhang,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

China is one of the countries with the largest citrus cultivation areas, and its citrus industry has received significant attention due to its substantial economic benefits. Traditional manual forestry surveys and remote sensing image classification tasks are labor-intensive and time-consuming, resulting in low efficiency. Remote sensing technology holds great potential for obtaining spatial information on citrus orchards on a large scale. This study proposes a lightweight model for citrus plantation extraction that combines the DeepLabV3+ model with the convolutional block attention module (CBAM) attention mechanism, with a focus on the phenological growth characteristics of citrus in the Guangxi region. The objective is to address issues such as inaccurate extraction of citrus edges in high-resolution images, misclassification and omissions caused by intra-class differences, as well as the large number of network parameters and long training time found in classical semantic segmentation models. To reduce parameter count and improve training speed, the MobileNetV2 lightweight network is used as a replacement for the Xception backbone network in DeepLabV3+. Additionally, the CBAM is introduced to extract citrus features more accurately and efficiently. Moreover, in consideration of the growth characteristics of citrus, this study augments the feature input with additional channels to better capture and utilize key phenological features of citrus, thereby enhancing the accuracy of citrus recognition. The results demonstrate that the improved DeepLabV3+ model exhibits high reliability in citrus recognition and extraction, achieving an overall accuracy (OA) of 96.23%, a mean pixel accuracy (mPA) of 83.79%, and a mean intersection over union (mIoU) of 85.40%. These metrics represent an improvement of 11.16%, 14.88%, and 14.98%, respectively, compared to the original DeepLabV3+ model. Furthermore, when compared to classical semantic segmentation models, such as UNet and PSPNet, the proposed model achieves higher recognition accuracy. Additionally, the improved DeepLabV3+ model demonstrates a significant reduction in both parameters and training time. Generalization experiments conducted in Nanning, Guangxi Province, further validate the model’s strong generalization capabilities. Overall, this study emphasizes extraction accuracy, reduction in parameter count, adherence to timeliness requirements, and facilitation of rapid and accurate extraction of citrus plantation areas, presenting promising application prospects.

show abstract

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

Amiriebrahimabadi,

Rouhi,

Mansouri

2024

Arch Computat Methods Eng

View full text Add to dashboard Cite

A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Cited by 11 publications

References 43 publications

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing

Contact Info

Product

Resources

About