SC : Secondary Image Capture

The Secondary Capture (SC) Image Information Object Definition (IOD) specifies images that are converted from a non-DICOM format to a modality independent DICOM format.

Examples of types of equipment that create Secondary Capture Images include:

  • Video interfaces that convert an analog video signal into a digital image
  • Digital interfaces that are commonly used to transfer non-DICOM digital images from an imaging device to a laser printer
  • Film digitizers that convert an analog film image to digital data
  • Workstations that construct images that are sent out as a screen dump
  • Scanned documents and other bitmap images including hand-drawings
  • Synthesized images that are not modality-specific, such as cine-loops of 3D reconstructions

Originally, a single, relatively unconstrained, single-frame SC Image IOD was defined in the DICOM Standard. Though this IOD is retained and not retired since it is in common use, more specific IODs for particular categories of application are also defined.

The following IODs are all multi-frame. A single frame image is encoded as a multi-frame image with only one frame. The multi-frame SC IODs consist of:

  • Multi-frame Single Bit Secondary Capture Image IOD
  • Multi-frame Grayscale Byte Secondary Capture Image IOD
  • Multi-frame Grayscale Word Secondary Capture Image IOD
  • Multi-frame True Color Secondary Capture Image IOD

URL: http://dicom.nema.org/dicom/2013/output/chtml/part03/sect_A.8.html

Any ‘valid’ Dicom Image is a conglomeration of Pixel data and several mandatory data attributes. To generate a Secondary Capture (SC) Image from existing non-Dicom image then all that is required is to load the required image into a DicomImage using the import function and then adding on the appropriate attributes. Dicom Objects will automatically take care of all the attributes relating to the pixel data (Group 0x0028).

URL: https://www.medicalconnections.co.uk/kb/Creating-Secondary-Capture-Images/


Comments

Popular posts from this blog

LUL, LLL, RUL, RML, RLL

What are GGN and GGO?

筆記: AI 醫療大未來 - 台灣第一本智慧醫療關鍵報告 by 李友專