Unit 1423: Image Analysis and Processing

1. Introduction to Image Analysis and Processing

• Definition and scope
• Key principles and techniques
• Applications in medicine, engineering, computer science
• Historical perspective and recent advancements

2. Image Acquisition and Preprocessing

• Digital image capturing methods and devices
• Common image file formats and standards (JPEG, PNG, TIFF, DICOM)
• Preprocessing techniques:
  • Noise reduction (mean, median, Gaussian filters)
  • Contrast enhancement (histogram equalization, adaptive histogram equalization)
  • Image normalization and scaling

3. Image Segmentation

• Purpose and importance of segmentation
• Thresholding methods:
  • Global and adaptive thresholding
• Edge detection techniques:
  • Sobel, Canny, Prewitt operators
• Region-based segmentation:
  • Region growing, region splitting and merging
• Clustering approaches:
  • K-means, mean shift
• Evaluation of segmentation results

4. Feature Extraction and Representation

• Types of features:
  • Shape: boundaries, contours, moments
  • Texture: co-occurrence matrices, Gabor filters
  • Color: color histograms, color moments
  • Intensity: grayscale statistics
• Feature descriptors and dimensionality reduction

5. Image Filtering and Enhancement

• Linear filters:
  • Convolution, smoothing, sharpening
• Nonlinear filters:
  • Median, bilateral filters
• Spatial domain filtering techniques
• Frequency domain filtering:
  • Fourier transform, low-pass, high-pass filters
• Applications in noise reduction and detail enhancement

6. Morphological Image Processing

• Fundamentals of morphology
• Basic operations:
  • Dilation, erosion
• Compound operations:
  • Opening, closing
• Applications in shape analysis and noise removal

7. Image Registration and Fusion

• Concept and objectives of registration
• Types of registration:
  • Rigid, affine, non-rigid transformations
• Feature-based vs intensity-based registration
• Image fusion techniques:
  • Pixel-level, feature-level, decision-level fusion
• Applications in multi-modal imaging

8. Object Detection and Recognition

• Feature-based detection methods
• Template matching techniques
• Object tracking fundamentals
• Deep learning approaches:
  • CNN architectures for detection and recognition
• Performance metrics and challenges

9. Image Analysis in Medical Imaging

• Overview of medical imaging modalities:
  • MRI, CT, X-rays, Ultrasound
• Image processing applications in diagnosis and treatment planning
• Segmentation and feature extraction in medical images
• Challenges in medical image analysis

10. Image Classification and Machine Learning

• Introduction to machine learning for images
• Supervised learning algorithms:
  • SVM, decision trees, neural networks
• Unsupervised learning:
  • Clustering, dimensionality reduction
• Deep neural networks and CNNs
• Training, validation, and evaluation of models