Bounded-depth threshold circuits for computer-assisted CT image classification.
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 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 75h up to 230h 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.
Albrecht
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A
Department of Computer Science and Engineering, CUHK, Shatin, NT, Hong Kong.
Hein
E
E
Steinhöfel
K
K
Taupitz
M
M
Wong
C K
CK
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008113:Liver Neoplasms / Q000175:diagnosis; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed