Digital Image Processing, Display, and HIM

Digital Image Processing, Display, and Health Information Management

Purpose and Objectives

  • Objective of Module:

    • Explain digital image processing.

    • Identify preprocessing corrections on raw image data.

    • Describe histogram construction and analysis.

    • Explain automatic rescaling and lookup tables, and their roles in image quality.

    • Differentiate between vendor-specific and universal standard exposure indicators (EIs) and their importance in assessing digital image quality.

    • Explain default and post-processing operations affecting image display quality.

    • Discuss impacts of over- and reprocessing on diagnostic and archival quality of data.

    • Identify monitor features affecting displayed image quality.

Digital Image Processing

  • Definition:

    • Use of computer algorithms to manipulate digital images.

  • Stages of Processing:

    • Pre-processing: Prepares data for processing and display.

    • Post-processing: Involves operations applied before and after the image is processed.

Pre-processing and Post-processing

  • Pre-processing:

    • Fixes imperfections in the electronic data set.

    • Examples:

      • Interpolation and flatfielding corrections.

  • Post-processing:

    • Prepares the raw data for display.

    • Examples:

      • Histogram creation and analysis

      • Automatic rescaling

      • LUT application

Histogram Creation and Analysis

Histogram Basics

  • Definition:

    • Graphic representation of pixel values in the image data set.

  • Axes:

    • X-axis: range of pixel values.

    • Y-axis: frequency of each pixel value.

  • Interpretation:

    • Right side = high attenuation (dark), left side = low attenuation (bright).

    • Size and shape indicate image contrast.

Histogram Analysis

  • Purpose:

    • Assess pixel values before processing.

    • Compare with reference histogram for image evaluation.

  • Key Features:

    • Peak: Most frequent pixel intensity.

    • Spread: Indicates contrast level.

    • Skewness: Asymmetry, indicates over/underexposure.

    • Kurtosis: Indicates distribution 'peakness.'

Automatic Rescaling and Look Up Table (LUT) Application

Automatic Rescaling

  • Purpose:

    • Adjusts image brightness and contrast optimally for diagnostic quality.

  • Process:

    1. Histogram creation.

    2. Comparison with reference histogram.

    3. Rescaling pixel intensity values.

  • Limitation:

    • Does not compensate for noise due to underexposure.

Look Up Table (LUT) Application

  • Purpose:

    • Adjusts image contrast based on selected procedure.

  • Process:

    • The appropriate LUT is automatically selected to achieve desired image quality.

Exposure Indicators (EIs)

  • Purpose of EIs:

    • Measure radiation quantity reaching the detector.

  • Calculation:

    • Based on pixel values from histogram analysis, defining values of interest (VOI).

Types of EIs:

  • Various vendor-specific names for EI include:

    • Philips DR: EI 250-630

    • Siemens: EXI 150-630

    • Agfa: IgM 1.6 - 2.2

Deviation Index (DI)

  • Purpose:

    • Compared recorded EI to target EI for examination.

  • Formula:

    • DI = 10 × log10 (Actual EI / Target EI)

  • Interpretation:

    • DI of 0: no difference.

    • DI > 1.0 SD: overexposed, DI < 1.0 SD: underexposed.

Practical Standards to Avoid HIPAA or HITECH Violations

  • Safeguarding PHI:

    • Avoid public discussions and control workstation access.

  • Use Imaging Systems Securely:

    • Do not leave imaging monitors unattended.

  • Limit PHI Sharing:

    • Share only on a need-to-know basis; anonymize for research.

  • Secure Communications:

    • Maintain privacy in phone conversations; verify identity before disclosing PHI.