Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems

Smart Healthcare Through Signal and Image Processing

This collected work brings together fourteen focused chapters on signal and image processing techniques designed to support intelligent healthcare systems. Edited by E. Priya and V. Rajinikanth, the book moves from core medical imaging concepts to applied disease detection workflows.

The chapters examine real-world clinical challenges: brain tumor recognition from MRI, segmentation of breast thermal images, fusion of PET and CT data for lung cancer examination, detection of diabetic retinopathy in retinal fundus photographs, and identification of tuberculosis bacilli in sputum smear microscopy. Biomedical signal processing is equally central, with studies on EEG analysis for sudden unexpected death in epilepsy and surface EMG classification for prosthetic control.

Key Themes and Applications

Across the book, authors combine traditional image processing with machine learning and deep learning methods. The contributions include:

• Automated tumor detection and segmentation
• Multi-modal image fusion for more reliable diagnosis
• Thermal and mammography-based breast cancer analysis
• Wavelet-based medical image watermarking for security
• MEMS-based gripper design and upper-body exoskeleton simulation
• Nonlinear EEG and sEMG signal classification

Designed for Researchers and Practitioners

The editors have organized the material to benefit a broad technical audience. Researchers, engineers, and clinicians working in biomedical signal and image analysis will find detailed methodologies, validation metrics, and comparative studies. Undergraduate and postgraduate students will also appreciate the structured introduction to intelligent healthcare applications.