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dc.contributor.authorAlbrecht, A.
dc.contributor.authorHein, E.
dc.contributor.authorSteinhofel, K.
dc.contributor.authorTaupitz, M.
dc.contributor.authorWong, C.K.
dc.identifier.citationAlbrecht , A , Hein , E , Steinhofel , K , Taupitz , M & Wong , C K 2001 , ' Bounded-depth threshold circuits for computer-assisted CT image classification ' , Artificial Intelligence in Medicine , vol. 24 , no. 2 , pp. 179-192 .
dc.identifier.otherdspace: 2299/5347
dc.descriptionOriginal article can be found at Copyright Elsevier [Full text of this article is not available in the UHRA]
dc.description.abstractWe 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.en
dc.relation.ispartofArtificial Intelligence in Medicine
dc.subjectCT images
dc.subjectperceptron algorithm
dc.subjectSimulated annealing
dc.subjectcooling schedule
dc.subjectthreshold functions
dc.subjectfocal liver tumour
dc.titleBounded-depth threshold circuits for computer-assisted CT image classificationen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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