Bounded-depth threshold circuits for computer-assisted CT image classification

A. Albrecht, E. Hein, K. Steinhofel, M. Taupitz, C.K. Wong

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    9 Citations (Scopus)


    We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed, where the first layer of the sub-circuit is calculated by a new combination of the Perceptron algorithm with a special type of simulated annealing. The algorithm is evaluated for the case of liver tissue classification. A depth-five threshold circuit (with pre-processing: depth-seven) is calculated from 400 positive (abnormal findings) and 400 negative (normal liver tissue) examples. The examples are of size n=14,161 (119 ×119) with an 8 bit grey scale. On test sets of 100 positive and 100 negative examples (all different from the learning set) we obtain a correct classification close to 99%. The total sequential run-time to compute a depth-five circuit is about 75 h up to 230 h on a SUN Ultra 5/360 workstation, depending on the width of the threshold circuit at depth-three. In our computational experiments, the depth-five circuits were calculated from three simultaneous runs for depth-four circuits. The classification of a single image is performed within a few seconds.
    Original languageEnglish
    Pages (from-to)179-192
    JournalArtificial Intelligence in Medicine
    Issue number2
    Publication statusPublished - 2001


    • CT images
    • perceptron algorithm
    • Simulated annealing
    • logarithmic
    • cooling schedule
    • threshold functions
    • focal liver tumour


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