Computer-aided diagnosis of liver lesions using CT images: A systematic review
PV Nayantara, S Kamath, KN Manjunath… - Computers in Biology …, 2020 - Elsevier
Background Medical image processing has a strong footprint in radio diagnosis for the
detection of diseases from the images. Several computer-aided systems were researched in …
detection of diseases from the images. Several computer-aided systems were researched in …
Weakly-supervised network for detection of COVID-19 in chest CT scans
Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily …
Cross-modality guided contrast enhancement for improved liver tumor image segmentation
Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-
guided surgery. However, low-contrast CT images impede the performance of subsequent …
guided surgery. However, low-contrast CT images impede the performance of subsequent …
Accelerating B-spline interpolation on GPUs: Application to medical image registration
Background and Objective B-spline interpolation (BSI) is a popular technique in the context
of medical imaging due to its adaptability and robustness in 3D object modeling. A field that …
of medical imaging due to its adaptability and robustness in 3D object modeling. A field that …
Fast parallel vessel segmentation
Abstract Background and Objective: Accurate and fast vessel segmentation from liver slices
remain challenging and important tasks for clinicians. The algorithms from the literature are …
remain challenging and important tasks for clinicians. The algorithms from the literature are …
Accelerating Chan–Vese model with cross-modality guided contrast enhancement for liver segmentation
Accurate and fast liver segmentation remains a challenging and important task for clinicians.
Segmentation algorithms are slow and inaccurate due to noise and low quality images in …
Segmentation algorithms are slow and inaccurate due to noise and low quality images in …
Precise and parallel segmentation model (PPSM) via MCET using hybrid distributions
S Rawas, A El-Zaart - Applied Computing and Informatics, 2024 - emerald.com
Purpose Image segmentation is one of the most essential tasks in image processing
applications. It is a valuable tool in many oriented applications such as health-care systems …
applications. It is a valuable tool in many oriented applications such as health-care systems …
Cross-modal guidance assisted hierarchical learning based siamese network for mr image denoising
Cross-modal medical imaging techniques are predominantly being used in the clinical suite.
The ensemble learning methods using cross-modal medical imaging adds reliability to …
The ensemble learning methods using cross-modal medical imaging adds reliability to …
Towards an early diagnosis of Alzheimer disease: a precise and parallel image segmentation approach via derived hybrid cross entropy thresholding method
S Rawas, A El-Zaart - Multimedia Tools and Applications, 2022 - Springer
Alzheimer's disease (AD) is an irreversible and progressive brain disease causing brain
degenerative disorder and dementia. An early diagnosis of AD provides the individual an …
degenerative disorder and dementia. An early diagnosis of AD provides the individual an …
Multi‐core accelerated simulation of x‐ray projection based on Unigraphics NX model
S Zhang, S Zhang, Y Liu, Y Zhang… - Concurrency and …, 2022 - Wiley Online Library
In computed tomography, the simulation of x‐ray projection is very important for developing
and evaluating image reconstruction methods. Currently, the computer aided design models …
and evaluating image reconstruction methods. Currently, the computer aided design models …