1 Definition 2 Functional Requirements 3 Syntax 4 Semantics

1 Definition

The Medical Image Processing Type defines the allowable operations, methods, and processing descriptors used for Medical Imaging data (e.g., MRI, CT, PET, Ultrasound).
It reuses Common Definitions for:

  • Header
  • Algorithm
  • Algorithms
  • FeatureClass
  • Features.

2 Functional Requirements

The Medical Image Processing Type shall:

  • Fix Domain = MedicalImaging.
  • Validate Operation against medical‑imaging‑specific enumerations.
  • Validate Method against recognised classical, ML‑based, and deep‑learning imaging techniques.
  • Allow Algorithm to be a string ID or an AlgorithmObject.
  • Allow Algorithms to be an array of Algorithm items.
  • Require Features to be a non‑empty array of unique strings.

3 Syntax

https://schemas.mpai.community/AIH1/V1.0/data/MedicalImageProcessingType.json

4 Semantics

Label Description
Header Medical Image Processing Type Header, Standard “AIH‑MPT‑Vx.y”.
Domain Constant value "MedicalImaging". Applies exclusively to medical images. No other value permitted.
Operation Specifies the medical‑image processing step. Enumerated list includes: Segmentation, Registration, Denoising, Enhancement, LesionDetection, Classification, Quantification, Reconstruction, MotionCorrection, FeatureExtraction.
Segmentation Partitioning an image into anatomical or functional regions (e.g., organs, lesions).
Registration Aligning one or more images into a shared spatial reference frame.
Denoising Reducing noise while preserving clinically relevant structures.
Enhancement Improving contrast, sharpness, or visibility of image content.
LesionDetection Identifying tumors, nodules, or abnormalities.
Classification Assigning diagnostic or morphological labels to images or ROIs.
Quantification Computing measurable biomarkers (volume, density, radiomics statistics).
Reconstruction Producing images from raw sensor data (e.g., MRI k‑space, CT sinograms).
MotionCorrection Correcting motion artefacts from patient or physiological movement.
FeatureExtraction Deriving radiomic, deep‑feature, or structural descriptors from images.
Method Processing technique used to implement the operation. Must be one of: OtsuThresholding, UNet, ResNet, FLIRT, ANTs, SIFT, SURF, NonLocalMeans, BilateralFilter, HistogramEqualization, CNN, Transformer, LevelSet, GraphCut.
OtsuThresholding Global thresholding based on histogram variance minimisation.
UNet CNN model for segmentation of medical images.
ResNet Deep residual network for classification or feature learning.
FLIRT Linear registration tool (FSL).
ANTs Nonlinear registration and normalization framework.
SIFT Local keypoint detector and descriptor for matching.
SURF Fast keypoint detector/descriptor for feature matching.
NonLocalMeans Patch‑based denoising preserving anatomical detail.
BilateralFilter Edge‑preserving smoothing filter.
HistogramEqualization Intensity redistribution to enhance contrast.
CNN Convolutional neural network‑based image processing.
Transformer Attention‑based deep‑learning model for medical imaging.
LevelSet Contour‑evolution method for segmentation.
GraphCut Graph‑optimization‑based segmentation technique.
Algorithm String identifier or an AlgorithmObject defined in CommonDefinitions.
AlgorithmObject.Name Required name of the algorithm (e.g., “UNet‑Baseline”, “ANTs‑SyN”).
AlgorithmObject.Version Optional version ID.
AlgorithmObject.Params Free‑form configuration parameters for the algorithm.
Algorithms Array of Algorithm entries (string ID or AlgorithmObject).
FeatureClass Category describing extracted imaging features (e.g., radiomics, morphologic, deep features).
Features Non‑empty array of unique feature names (e.g., lesion_volume, entropy, Hounsfield_mean).
Trace Provenance information and Time.
DescrMetadata Descriptive Metadata