Small nodule detectability evaluation using a generalized scan-statistic model.
In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer.
Popescu
Lucreţiu M
LM
Department of Radiology, University of Pennsylvania, 423 Guardian Drive, 4th floor Blockley Hall, Philadelphia, PA 19104-6021, USA. popescu@mipg.upenn.edu
Lewitt
Robert M
RM
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D003952:Diagnostic Imaging / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D015233:Models, Statistical; D015588:Observer Variation; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data