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

doi:10.1016/S0933-3657(01)00101-4

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

2002-02-01

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DOI：10.1016/S0933-3657(01)00101-4

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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 preprocessing: 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 x 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. (C) 2002 Elsevier Science B.V. All rights reserved.

Keywords:

CT images

perceptron algorithm

simulated annealing

logarithmic

cooling schedule

threshold functions

focal liver tumour

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

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