A5017608077- Advanced X-ray and CT Imaging
- Cardiac Imaging and Diagnostics
- Medical Image Segmentation Techniques
- Radiomics and Machine Learning in Medical Imaging
- Image Processing Techniques and Applications
- Cardiovascular Function and Risk Factors
- EEG and Brain-Computer Interfaces
- Advanced MRI Techniques and Applications
- Cardiac Valve Diseases and Treatments
- IoT and Edge/Fog Computing
- Stroke Rehabilitation and Recovery
- Advanced Image Processing Techniques
- Image and Object Detection Techniques
- Image and Signal Denoising Methods
- Advanced Neural Network Applications
Tun Hussein Onn University of Malaysia
2021-2023
Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional functions diagnosing cardiovascular diseases. LV clinical metrics such as volume, mass ejection fraction (EF) are frequently extracted based on the from short-axis MRI images. Manual to assess tedious time-consuming medical experts diagnose pathologies. Therefore, fully automated technique required assist in working more efficiently.This paper proposes...
Cardiovascular diseases (CVDs) are considered one of the leading causes death worldwide. Myocardial infarction (MI) is deadliest cardiac that require more consideration. Recently, magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation left ventricle (LV) and myocardium from MRI images vital in detecting MI disease at its early stages. automatic LV still challenging due to complex structures images, inhomogeneous shape moving...
Abstract Arm rehabilitation activities need to be monitored continuously in terms of analysis by experts within sufficient information discover arm dysfunction and disorders such as stroke early. Although there are numerous previous researches about the home-based procedures performance, some drawbacks still exist. For example, current devices too complicated required supervision qualified therapists rather than their high prices. Moreover, data from these take much time sent doctors for...
The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac using convolutional neural networks for extraction region interest (ROI). precise LV’s ROI from MRI crucial detecting heart disorders via segmentation or registration. Nevertheless, this task appears be intricate due diversities size and shape LV scattering surrounding tissues across different slices. Thus, study proposed region-based network (Faster R-CNN)...