This area focuses on technologies and techniques to visualize and analyze the internal structures of the human body and process physiological signals for better understanding and diagnosis of medical conditions.
- Biomedical Imaging and Signal Processing.jpg (8.25 KiB) Viewed 350 times
1. Medical Imaging Technologies
Medical imaging involves creating visual representations of the body’s interior for clinical analysis and medical intervention.
Key imaging modalities include:
- MRI (Magnetic Resonance Imaging): Uses strong magnetic fields and radio waves to produce detailed images of soft tissues like the brain, muscles, and organs.
- CT (Computed Tomography): Combines X-rays with computer processing to generate cross-sectional images of bones, blood vessels, and tissues.
- X-ray: Provides quick imaging of bones and detecting fractures or infections.
- Ultrasound: Uses high-frequency sound waves to capture live images of organs, tissues, and blood flow, commonly used in pregnancy and heart diagnostics.
- PET (Positron Emission Tomography): Tracks metabolic activity by detecting gamma rays from injected radioactive tracers, often used in cancer diagnosis.
- Biomedical Imaging and Signal Processing 1.jpg (14.32 KiB) Viewed 350 times
2. Signal Processing in Healthcare
Biomedical signal processing analyzes physiological signals to extract useful information for diagnosis, monitoring, and treatment. Examples include:
- ECG (Electrocardiography): Records electrical activity of the heart to diagnose arrhythmias, heart attacks, and other cardiac conditions.
- EEG (Electroencephalography): Monitors brain activity to detect seizures, sleep disorders, or brain injuries.
- EMG (Electromyography): Measures muscle electrical activity to evaluate neuromuscular health.
3. Role of Artificial Intelligence
AI and machine learning have become integral in analyzing imaging data and biomedical signals, offering:
- Automated image segmentation and diagnosis (e.g., detecting tumors).
- Predictive analysis for diseases using signal trends.
- Enhanced speed and accuracy in interpreting complex datasets.
- Diagnosis: Early detection of diseases like cancer, stroke, or heart conditions.
- Treatment Planning: Real-time imaging during surgeries or radiation therapy.
- Monitoring: Continuous health tracking through wearable sensors.