title: Evaluation of internal noise methods for Hotelling observer models.
abstract: The inclusion of internal noise in model observers is a common method to allow for quantitative comparisons between human and model observer performance in visual detection tasks. In this article, we studied two different strategies for inserting internal noise into Hotelling model observers. In the first strategy, internal noise was added to the output of individual channels: (a) Independent nonuniform channel noise, (b) independent uniform channel noise. In the second strategy, internal noise was added to the decision variable arising from the combination of channel responses. The standard deviation of the zero mean internal noise was either constant or proportional to: (a) the decision variable's standard deviation due to the external noise, (b) the decision variable's variance caused by the external noise, (c) the decision variable magnitude on a trial to trial basis. We tested three model observers: square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO) using a four alternative forced choice (4AFC) signal known exactly but variable task with a simulated signal embedded in real x-ray coronary angiogram backgrounds. The results showed that the internal noise method that led to the best prediction of human performance differed across the studied model observers. The CHO model best predicted human observer performance with the channel internal noise. The HO and LGHO best predicted human observer performance with the decision variable internal noise. The present results might guide researchers with the choice of methods to include internal noise into Hotelling model observers when evaluating and optimizing medical image quality.
authors: Vision and Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California 93106, USA. yanizh@yahoo.com
authors: Yani
authors: Y
authors: Zhang
authors: Binh T
authors: BT
authors: Pham
authors: Miguel P
authors: MP
authors: Eckstein
mesh_terms: D000465:Algorithms; D016477:Artifacts; D017023:Coronary Angiography / Q000295:instrumentation* / Q000379:methods*; D044962:Data Compression; D005069:Evaluation Studies as Topic; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results