Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >

Nazish Naheed¹, Muhammad Shaheen¹, Sajid Ali Khan¹, Mohammed Alawairdhi^2,*, Muhammad Attique Khan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380 - 18 September 2020

Abstract In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data… More >

Yongjie Zhang¹, Wenyan Wang¹, Xinghua Liang¹, Yuri Bazilevs², Ming-Chen Hsu², Trond Kvamsdal³, Reidar Brekken⁴, Jørgen Isaksen⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.42, No.2, pp. 131-150, 2009, DOI:10.3970/cmes.2009.042.131

Abstract This paper describes a comprehensive and high-fidelity finite element meshing approach for patient-specific arterial geometries from medical imaging data, with emphasis on cerebral aneurysm configurations. The meshes contain both the blood volume and solid arterial wall, and are compatible at the fluid-solid interface. There are four main stages for this meshing method: 1) Image segmentation and geometric model construction; 2) Tetrahedral mesh generation for the fluid volume using the octree-based method; 3) Mesh quality improvement stage, in which edge-contraction, pillowing, optimization, geometric flow smoothing, and mesh cutting are applied to the fluid mesh; and 4) More >