Tumor detection in nonstationary backgrounds

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Abstract
The authors introduce two detectors which they use to locate simulated tumors of fixed size in clinical gamma-ray images. The first method was conceived when it was observed that small tumors possess an identifiable signature in curvature feature space, where "curvature" is the local curvature of the image data when viewed as a relief map. Computed curvature values are mapped to a normalized significance space using a windowed statistic. The resulting test statistic is thresholded at a chosen level of significance to give a positive detection. Nonuniform anatomic background activity is effectively suppressed. The second detector is an adaptive prewhitening matched filter, which uses a form of preprocessing known as statistical scaling to adaptively prewhiten the background. Tests are performed using simulated Gaussian-shaped tumors superimposed on twelve clinical gamma ray images. When the tumors to be detected are small-less than 3 pixels in diameter-the curvature detector out-performs the matched filter in true positive/false positive tests. 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-background is >/=2. At larger tumor sizes the best performance is displayed by a different form of matched filter, namely the statistical correlation function proposed by Pratt (1991).

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