Shaoyong Hong¹, Bo Yang², Yan Chen², Hao Quan³, Shan Liu⁴, Minyi Tang^5,*, Jiawei Tian^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1091-1112, 2025, DOI:10.32604/cmes.2025.066165 - 31 July 2025

Abstract 3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and… More >

Dongheng Ye¹, Heping Li^2,3, Ning An^2,3, Jian Cheng^2,3, Liang Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3731-3747, 2025, DOI:10.32604/cmc.2025.063693 - 03 July 2025

Abstract The newly emerging neural radiance fields (NeRF) methods can implicitly fulfill three-dimensional (3D) reconstruction via training a neural network to render novel-view images of a given scene with given posed images. The Instant Neural Graphics Primitives (Instant-NGP) method further improves the position encoding of NeRF. It obtains state-of-the-art efficiency. However, only a local pixel-wised loss is considered when training the Instant-NGP while overlooking the nonlocal structural information between pixels. Despite a good quantitative result, it leads to a poor visual effect, especially the completeness. Inspired by the stochastic structural similarity (S3IM) method that exploits nonlocal… More >

Nguyen Khac Toan¹, Ho Nguyen Anh Tuan², Nguyen Truong Thinh^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1273-1295, 2025, DOI:10.32604/cmes.2025.064218 - 30 May 2025

Abstract This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods. The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery. The approach leverages advanced image processing techniques, 3D Morphable Models (3DMM), and deformation techniques to overcome the limitations of deep learning models, particularly addressing the interpretability issues commonly encountered in medical applications. The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm. Sub-landmarks… More > Graphic Abstract

Rana Mohamed^1,2,*, Mostafa Elgendy¹, Mohamed Taha¹

Computer Systems Science and Engineering, Vol.49, pp. 213-238, 2025, DOI:10.32604/csse.2024.059491 - 10 January 2025

Abstract Globally, liver cancer ranks as the sixth most frequent malignancy cancer. The importance of early detection is undeniable, as liver cancer is the fifth most common disease in men and the ninth most common cancer in women. Recent advances in imaging, biomarker discovery, and genetic profiling have greatly enhanced the ability to diagnose liver cancer. Early identification is vital since liver cancer is often asymptomatic, making diagnosis difficult. Imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonography can be used to identify liver cancer once a sample of liver tissue is… More >

Yen-Jen Lai¹, I-Ling Chang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012918

Abstract Biomechanical research reveals that the geometric shapes and dynamic behaviors of organ tissues play a pivotal role in determining their mechanical properties. Recent advancements in time-correlated imaging technologies, such as Computed Tomography (4D-CT) and Magnetic Resonance Imaging (4D-MRI), have enabled the non-invasive capture of both geometric data and dynamic information over time. However, the manual segmentation of these extensive datasets proves to be laborious and expensive. This study introduces an automated workflow designed for image segmentation and classification within 4D-CT scans, with a specific focus on the bone structures surrounding the Trapeziometacarpal (TMC) joint in More >

Hoi Chuen Cheng, Frederick Ziyang Hong, Babar Hussain, Yiru Wang, Chik Patrick Yue^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 161-181, 2024, DOI:10.32604/cmc.2024.053079 - 15 October 2024

Abstract Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies. This work contributes to a framework addressing localization, coordination, and vision processing for multi-agent reconstruction. A system architecture fusing visible light positioning, multi-agent path finding via reinforcement learning, and 360° camera techniques for 3D reconstruction is proposed. Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure. Meanwhile, a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem, with communications among agents optimized. Our 3D reconstruction pipeline utilizes equirectangular projection from 360° cameras to More >

Junpeng Lyu¹, Hai Mei^1,2, Liping Zu¹, Lisheng Liu^1,2,*, Liangliang Chu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 391-410, 2024, DOI:10.32604/cmes.2023.030614 - 22 September 2023

Abstract This article proposes a modeling method for C/C-ZrC composite materials. According to the superposition of Gaussian random field, the original gray model is obtained, and the threshold segmentation method is used to generate the C-ZrC inclusion model. Finally, the fiber structure is added to construct the microstructure of the three-phase plain weave composite. The reconstructed inclusions can meet the randomness of the shape and have a uniform distribution. Using an algorithm based on asymptotic homogenization and finite element method, the equivalent thermal conductivity prediction of the microstructure finite element model was carried out, and the… More >

Shariar Md Imtiaz¹, Ki-Chul Kwon¹, F. M. Fahmid Hossain¹, Md. Biddut Hossain¹, Rupali Kiran Shinde¹, Sang-Keun Gil², Nam Kim^1,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205 - 09 November 2023

Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field… More >

Sunyoung Bu¹, Suwon Lee^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3083-3096, 2023, DOI:10.32604/cmes.2023.024460 - 09 March 2023

Abstract Reconstructing a three-dimensional (3D) environment is an indispensable technique to make augmented reality and augmented virtuality feasible. A Kinect device is an efficient tool for reconstructing 3D environments, and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces. To employ multiple devices simultaneously, Kinect devices need to be calibrated with respect to each other. There are several schemes available that calibrate 3D images generated from multiple Kinect devices, including the marker detection method. In this study, we introduce a markerless calibration technique for Azure Kinect devices that avoids the More > Graphic Abstract

Tao Zhang^1,*, Yi Cao²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083 - 21 April 2022

Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved More >