Grey Wolf Optimizer for Multimodal Medical Image Registration

Dida, Hedifa; Charif, Fella; Benchabane, Abderrazak

doi:10.1109/icds50568.2020.9268771

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gray wolves in nature have a leadership structure and a hunting process that the Gray Wolf Optimization algorithm resembles. Wolves are divided into alpha, beta, delta, and omega wolves [25]. The most important characteristic of the wolf's community is that it has a strict social hierarchy and is divided into four levels.…”

Section: Swarm Optimization 331 Gray Wolf Optimizationmentioning

confidence: 99%

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Ahmed,

Kashmola

2023

Iraqi Journal of Science

View full text Add to dashboard Cite

The main task of creating new digital images of different skin diseases is to increase the resolution of the specific textures and colors of each skin disease. In this paper, the performance of generative adversarial networks has been optimized to generate multicolor and histological color digital images of a variety of skin diseases (melanoma, birthmarks, and basal cell carcinomas). Two architectures for generative adversarial networks were built using two models: the first is a model for generating new images of dermatology through training processes, and the second is a discrimination model whose main task is to identify the generated digital images as either real or fake. The gray wolf swarm algorithm and the whale swarm algorithm were relied on to generate values that improve the performance of GANs and insert them into the generator instead of random values, which in turn worked to reduce the loss values for the generated images. Loss values were adopted as a measure of optimizations for each epoch, and the fastest access time to actual digital images for each skin disease was adopted. Before the optimization operations, 50% accurate images of skin diseases were obtained; after the optimization operations, 98% accurate images of skin diseases were obtained.

show abstract

Section: Swarm Optimization 331 Gray Wolf Optimizationmentioning

confidence: 99%

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Ahmed,

Kashmola

2023

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…In 3D reconstruction from multi-view images, there are several processes, including image registration, which is the transforming process of different sets of data into one coordinate system. There are existing works on 2D medical image registration [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Since point cloud coordinates are used in our 3D teeth reconstruction, the 3D registration is more proper.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Wongkhuenkaew,

Auephanwiriyakul,

Chaiworawitkul

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar.

show abstract

Medical Image Processing by Swarm-Based Methods

Delgado

Gallego

2023

EAI/Springer Innovations in Communication and Computing

View full text Add to dashboard Cite

Grey Wolf Optimizer for Multimodal Medical Image Registration

Cited by 8 publications

References 13 publications

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Medical Image Processing by Swarm-Based Methods

Contact Info

Product

Resources

About