Evaluation of Collimation Prediction Based on Depth Images and Automated Landmark Detection for Routine Clinical Chest X-Ray Exams
The aim of this study was to evaluate the performance of a machine learning algorithm applied to depth images for the automated computation of X-ray beam collimation parameters in radiographic chest examinations including posterior-anterior (PA) and left-lateral (LAT) views. Our approach used as intermediate step a trained classifier for the detection of internal lung landmarks that were defined on X-ray images acquired simultaneously with the depth image. The landmark detection algorithm was evaluated retrospectively in a 5-fold cross validation experiment on the basis of 89 patient data sets acquired in clinical settings. Two auto-collimation algorithms were devised and their results were compared to the reference lung bounding boxes defined on the X-ray images and to the manual collimation parameters set by the radiologic technologists.
KeywordsBoosted tree classifiers Gentle AdaBoost Anatomical landmarks Detection Constellation model Multivariate regression X-ray beam collimation
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