6.3 Image Segmentation and Registration

Image segmentation and registration are crucial techniques in biomedical image processing. They help identify and align important structures in medical scans, making diagnosis and treatment planning more accurate. These methods range from simple thresholding to complex algorithms that can handle tricky cases.

Segmentation separates images into meaningful parts, while registration aligns different images of the same subject. Together, they enable doctors to compare scans over time, combine info from different imaging types, and even use pre-labeled atlases to automatically identify structures in new scans.

Image Segmentation Techniques

Intensity-Based Segmentation Methods

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• Thresholding separates image regions based on pixel intensity values
  • Utilizes a predetermined threshold value to classify pixels
  • Global thresholding applies a single threshold to the entire image
  • Adaptive thresholding adjusts the threshold locally based on surrounding pixel intensities
• Edge detection identifies object boundaries by detecting abrupt changes in intensity
  • Gradient-based methods (Sobel, Prewitt) calculate intensity differences between neighboring pixels
  • Laplacian-based methods detect zero-crossings in the second derivative of the image
• Region growing groups similar pixels into regions starting from seed points
  • Iteratively expands regions by adding neighboring pixels that meet similarity criteria
  • Criteria can include intensity, texture, or color similarities

Advanced Segmentation Algorithms

• Watershed algorithm treats the image as a topographic surface
  • Considers pixel intensities as elevation values
  • Simulates flooding from local minima to create segmented regions
  • Effectively separates touching objects with distinct boundaries
• Active contours (snakes) evolve a deformable curve to fit object boundaries
  • Minimizes an energy function combining internal forces (curve smoothness) and external forces (image gradients)
  • Can handle complex shapes and adapt to image noise
  • Requires initial contour placement near the target object

Registration Methods

Linear Transformation Techniques

• Rigid registration aligns images using only translation and rotation
  • Preserves distances between points and angles between lines
  • Suitable for aligning images of rigid structures (bones, brain)
  • Typically uses 6 degrees of freedom (3 for translation, 3 for rotation)
• Affine registration extends rigid registration by allowing scaling and shearing
  • Preserves parallel lines but not necessarily angles or distances
  • Accounts for global differences in size or orientation between images
  • Uses 12 degrees of freedom (9 for affine matrix, 3 for translation)

Non-Linear Registration Approaches

• Deformable registration allows local deformations to align images
  • Models complex anatomical variations between subjects or over time
  • Employs a dense displacement field or parametric transformations (B-splines, thin-plate splines)
  • Requires regularization to ensure physically plausible deformations
• Feature-based registration aligns images using corresponding landmarks or structures
  • Identifies and matches distinctive features (corners, edges, anatomical landmarks)
  • Can be combined with intensity-based methods for improved accuracy
  • Particularly useful when images have different modalities or large deformations

Image Registration Concepts

Advanced Registration Techniques

• Atlas-based segmentation combines registration and segmentation
  • Registers a pre-segmented atlas to a target image
  • Transfers atlas labels to the target image based on the registration
  • Useful for automatic segmentation of complex anatomical structures
• Mutual information measures statistical dependency between image intensities
  • Widely used similarity metric for multi-modal image registration
  • Maximizes the shared information between registered images
  • Robust to differences in image contrast and intensity distributions

Practical Considerations in Registration

• Interpolation methods estimate intensity values at non-grid positions during transformation
  • Nearest neighbor interpolation assigns the closest pixel value (fast but can introduce artifacts)
  • Linear interpolation uses weighted average of neighboring pixels (balances speed and quality)
  • Higher-order methods (cubic, spline) provide smoother results but are computationally intensive
• Image fusion combines information from multiple registered images
  • Enhances visualization and analysis of complementary information
  • Methods include color overlay, alpha blending, and wavelet-based fusion
  • Applications include multimodal medical imaging and remote sensing