Evaluation of Image Analysis Techniques Without Requiring Ground Truth or Gold Standard

摘要

Observers often evaluate image analysis techniques by comparing their results with the corresponding ground truth or gold standard. Difficulties in making such assessments often occur when the ground truth or gold standard is either unknown or inaccurate. Motivated by commonly used image restoration approaches, we developed an image analysis technique, which, instead of assessing the obtained results, directly assesses the technique itself by testing its validity it with the fundamental imaging principles which are well defined. We conducted a statistical investigation into MR imaging, starting from the data domain and proceeding to the image domain, and derived several intrinsic statistical properties of MR images. Based on them, we further proved a Finite Normal Mixture (FNM) model (in terms of pixel intensities and their independence) and a Markov random field (MRF) model (in terms of pixel intensities and their correlation) for MR images, and developed Expectation-Maximization (EM) and Iterated Conditional Modes (ICM) algorithms for FNM and MRF model-based image analysis. The results obtained by applying these algorithms to real MR images demonstrated that this image analysis technique can generate results which accurately fit the true objects.