Method and system for automatic classification of radiographic images having different acquisition characteristics

A method for classifying at least one radiographic images according to a set of characteristics of image acquisition by applying a machine learning model generated for automatic classification of radiographic images, said machine learning model defining at least two reference clusters of said radiographic images acquired by various acquisition protocols, comprising:

• Receiving at least one radiographic image,
• Providing one of the at least one radiographic image as a candidate image for classification,
• Detecting and segmenting in said candidate image at least one region of interest as candidate reference ROI,
• Measuring at least one radiomic feature per candidate reference ROI,
• Identifying valid candidate reference ROIs from a plurality of candidate reference ROIs based on the measured radiomics values,
• Comparing the measured radiomics values of each said valid candidate reference ROIs to each of the radiomics value of each of said at least two reference clusters, and
• Classifying the candidate image into one of the at least two reference clusters based on the comparison.

A method for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols, comprising:

• Providing a plurality of radiographic images of at least two different patients, the radiographic images of each of the at least two different patients being acquired with acquisition protocols different from each other,
• Detecting and segmenting in each of said radiographic images at least one regions of interest as reference ROI,
• Measuring at least one radiomic feature per reference ROI,
• Identifying valid reference ROIs from a plurality of reference ROIs based on the measured radiomics values,
• Clustering the measured radiomics values of said valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition,
• wherein detecting and segmenting in each of said radiographic images at least one regions of interest as reference ROI comprises, if the radiographic images are phantom images and/or mixed images each combining a patient and a pocket phantom, segmenting various phantom-derived structure of interest based on a reference material, and segmenting and detecting ROIs in the segmented PDSIs.

A method for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols, comprising:

• Providing a plurality of radiographic images of at least two different patients, the radiographic images of each of the at least two different patients being acquired with acquisition protocols different from each other,
• Detecting and segmenting in each of said radiographic images at least one regions of interest as reference ROI,
• Measuring at least one radiomic feature per reference ROI,
• Identifying valid reference ROIs from a plurality of reference ROIs based on the measured radiomics values,
• Clustering the measured radiomics values of said valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition,
• wherein identifying valid reference ROIs from a plurality of reference ROIs further comprises comparing the measured radiomics values to a predetermined range of radiomics values chosen from a set of reference value ranges and determining a valid reference ROI if the measured radiomics values thereof fall into the predetermined range of radiomics values.