5.5 Image Processing and Analysis

Digital image processing transforms pixels into meaningful data. It's the backbone of medical imaging, turning raw scans into diagnostic tools. Understanding pixels, resolution, and color channels is crucial for interpreting medical images accurately.

Image enhancement and segmentation are key techniques in medical imaging. They improve image quality and isolate specific structures, enabling more precise diagnoses. These methods help doctors spot abnormalities and make informed treatment decisions.

Digital Image Processing Fundamentals

Fundamentals of digital image processing

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• Digital images composed of pixels arranged in a 2D grid
  • Each pixel represents a small area of the image
  • Pixel values indicate intensity or color at that location
• Pixel representation
  • Grayscale images: Each pixel represented by a single intensity value
    • Commonly an 8-bit integer (0-255)
    • 0 represents black, 255 represents white
  • Color images: Each pixel represented by multiple color channels (RGB)
    • Red, Green, and Blue channels combined to form the final color
    • Each channel typically has an 8-bit integer value (0-255)
• Image resolution
  • Spatial resolution: Number of pixels in the image (width x height)
    • Higher spatial resolution means more detail captured (megapixels)
  • Bit depth: Number of bits used to represent each pixel
    • Higher bit depth allows for greater range of intensity or color values (8-bit, 16-bit)

Image Enhancement and Segmentation

Basic image enhancement techniques

• Contrast adjustment
  • Modifies dynamic range of pixel intensities to improve visual perception
  • Histogram equalization: Redistributes pixel intensities to cover full range
  • Contrast stretching: Linearly maps original intensity range to desired range
• Noise reduction
  • Removes unwanted distortions or artifacts from image
  • Gaussian filtering: Applies 2D Gaussian kernel to smooth image
    • Reduces high-frequency noise while preserving edges
  • Median filtering: Replaces each pixel with median value of its local neighborhood
    • Effective for removing salt-and-pepper noise

Image segmentation and feature extraction

• Image segmentation
  • Partitions image into distinct regions or objects of interest
  • Thresholding: Separates pixels into foreground and background based on intensity
    • Global thresholding uses single threshold value for entire image
    • Adaptive thresholding varies threshold based on local image characteristics
  • Region growing: Groups pixels into regions based on similarity criteria
    • Starts from seed points and iteratively expands regions (watershed algorithm)
• Feature extraction
  • Identifies and quantifies relevant characteristics of segmented regions
  • Shape features: Area, perimeter, circularity, moments
  • Intensity features: Mean, median, standard deviation, histogram
  • Texture features: Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP)

Algorithm Evaluation and Validation

Performance evaluation of algorithms

• Performance metrics
  • Accuracy: Proportion of correctly classified or segmented pixels
  • Precision: Proportion of true positive pixels among all positive predictions
  • Recall (Sensitivity): Proportion of true positive pixels among all actual positive pixels
  • F1 score: Harmonic mean of precision and recall
    • $F1 = 2 * (precision * recall) / (precision + recall)$
  • Intersection over Union (IoU): Overlap between predicted and ground truth regions
    • $IoU = (True Positive) / (True Positive + False Positive + False Negative)$
• Validation methods
  • Ground truth comparison: Comparing algorithm's output with manually labeled data
  • Cross-validation: Dividing dataset into subsets for training and testing
    • k-fold cross-validation: Partitions data into k equal-sized subsets
    • Leave-one-out cross-validation: Uses each single instance as test set
  • Visual assessment: Qualitative evaluation of algorithm's output by experts