Topic 34: Argumentative texts. Structure and characteristics.

design patterns (structural)

LP2 - Eye structure - Ear Structure

Organizational Structures and Systems

DNA Structure and Function

Structural Systems

Introduction to the Theory of Market Structure

Parliamentary Structure and Function

FORM/STRUCTURE + CONTEXT + THEMES

FORM/STRUCTURE + CONTEXT + THEMES

FORM/STRUCTURE + CONTEXT + THEMES

Structure and Function of Proteins (LEC)

body systems and cells structure

Renal Anatomy and Nephron Structure: Key Concepts for Biology

Genetics Ch 9: Molecular Structure of DNA and RNA

Amino Acids - Structure to full name

Judicial System: Court Structure, Jurisdiction, and Key Legal Terms Save Groups

Topic 2 : Atomic Structure

Cardiovascular Anatomy and Physiology: Heart Valves, Blood Flow, and Vessel Structure

Quiz 3 Study Guide – Digital Radiography ⸻ Preprocessing vs Postprocessing • Preprocessing = happens before the image is displayed • Corrects raw data from the detector • Examples: flat-field correction, dead pixel correction • Postprocessing = happens after image acquisition • Adjusts image appearance • Examples: brightness, contrast, edge enhancement ⸻ Postprocessing Domains Spatial Domain • Works with pixel location • Affects detail/resolution • Example: smoothing, edge enhancement Intensity Domain • Works with pixel brightness values • Affects contrast • Example: windowing, LUT Frequency Domain • Works with patterns (frequencies) in the image • Separates noise vs detail • Example: filtering (remove noise or enhance edges) ⸻ Histogram Analysis What the Histogram Represents • Graph of pixel intensity distribution • X-axis = brightness (black → white) • Y-axis = number of pixels ⸻ Types of Histogram Analysis • Type 1 • Simple exams (extremities) • Single peak • Type 2 • Two main tissue types (chest) • Two peaks • Type 3 • Complex anatomy (abdomen) • Multiple peaks • Neural (AI-based) • Uses pattern recognition instead of fixed shapes ⸻ Construction of Histogram 1. Image acquired 2. Exposure field recognized 3. Pixels analyzed 4. Histogram created from pixel values ⸻ Histogram Analysis Errors • Wrong body part selected • Collimation too wide/narrow • Artifacts (prosthetics, shielding) • Multiple exposures on one plate 👉 Leads to incorrect brightness/contrast ⸻ Look-Up Table (LUT) • Converts pixel values → visible grayscale • Controls contrast and brightness appearance • Different LUTs = different exam types ⸻ Dynamic Range Compression (DRC) • Reduces wide exposure range into visible range • Helps see both dark & light areas ⚠️ Effects: • Can reduce contrast • Can hide pathology if overused ⸻ Smoothing • Reduces noise • Makes image look softer • ↓ spatial resolution ⸻ Edge Enhancement • Increases sharpness • Highlights borders • Can increase noise/artifacts ⸻ SNR vs CNR • SNR (Signal-to-Noise Ratio) • Signal vs background noise • Higher = cleaner image • CNR (Contrast-to-Noise Ratio) • Difference between structures vs noise • Higher = better visibility of anatomy ⸻ Dose Creep & Exposure Index (EI) • Dose creep • Gradual increase in exposure over time • Happens because images still look good even at higher dose • Exposure Index (EI) • Indicates how much radiation reached detector • Used to monitor proper exposure ⸻ Segmentation • Identifies area of interest • Removes background from analysis • Important for accurate histogram ⸻ Grid Line Suppression • Removes visible grid lines digitally • Prevents moiré patterns ⸻ Kernels • Mathematical filters applied to image • Types: • Smooth (reduce noise) • Sharp (increase detail) ⸻ Postprocessing Controls (Tech Can Adjust) • Window level (brightness) • Window width (contrast) • Magnification • Edge enhancement • Smoothing ⸻ Sequence of Preprocessing Events 1. Exposure detection 2. Analog → digital conversion 3. Flat-field correction 4. Dead pixel correction 5. Exposure field recognition 6. Histogram creation 7