We introduce two detectors which we use to locate simulated tumors of
fixed size in clinical gamma-ray images. The first method was conceive
d when it was observed that small tumors possess an identifiable signa
ture in curvature feature space, where ''curvature'' is the local curv
ature of the image data when viewed as a relief map. Computed curvatur
e values are mapped to a normalized significance space using a windowe
d t-statistic. The resulting test statistic is thresholded at a chosen
level of significance to give a positive detection. Nonuniform anatom
ic background activity is effectively suppressed. The second detector
is an adaptive prewhitening matched filter, which uses a form of prepr
ocessing known as statistical scaling to adaptively prewhiten the back
ground. Tests are performed using simulated Gaussian-shaped tumors sup
erimposed on twelve clinical gamma-ray images. When the tumors to be d
etected are small-less than 3 pixels in diameter - the curvature detec
tor out-performs the matched filter in true positive/false positive te
sts. A mean true positive rate of 95% at one false positive per image
is achieved when the local signal-to-noise ratio of the tumor-backgrou
nd is greater-than-or-equal-to 2. At larger tumor sizes the best perfo
rmance is displayed by a different form of matched filter, namely the
statistical correlation function proposed by Pratt.