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 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
F 1 = 2 ∗ ( p r e c i s i o n ∗ r e c a l l ) / ( p r e c i s i o n + r e c a l l ) F1 = 2 * (precision * recall) / (precision + recall) F 1 = 2 ∗ ( p rec i s i o n ∗ rec a ll ) / ( p rec i s i o n + rec a ll )
Intersection over Union (IoU) : Overlap between predicted and ground truth regions
I o U = ( T r u e P o s i t i v e ) / ( T r u e P o s i t i v e + F a l s e P o s i t i v e + F a l s e N e g a t i v e ) IoU = (True Positive) / (True Positive + False Positive + False Negative) I o U = ( T r u e P os i t i v e ) / ( T r u e P os i t i v e + F a l se P os i t i v e + F a l se N e g a t i v e )
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