<title>Tumor detection in gamma-ray images</title>

Abstract

We describe a nonlinear detector which uses student's t-test to locate tumors occurring in anatomic background. The detector computes the significance of any observed difference between the mean of features extracted from a small, circular search window and the mean of features belonging to an outer, concentric background window. The t-test is applied to search windows at every pixel location in the image. The t-statistic computed from the sample means and variances of the inner and outer regions is thresholded at a chosen significance level to give a positive detection. The response of the detector peaks when the inner window coincides with a bright spot of the same size. Nonuniform anatomic background activity is effectively suppressed, except for structure of the same size and shape as the tumors being sought. Because the t-statistic is a true measure of significance, it can be applied to any set of features which are likely to distinguish tumors. We apply the test to two features, one related to object intensity and the other to object shape. A final determination on the presence and location of tumors is made by a simple combination of the significance levels generated from each feature. Tests are performed using simulated tumors superimposed on clinical images. Performance curves resembling standard receiver-operating-characteristic (ROC) plots show a slight improvement over the prewhitening matched filter. Unlike the matched filter, however, the t-test detector assumes nothing specific about the tumor apart from its size.