dc.contributor.author | Albrecht, A. | |
dc.contributor.author | Steinhofel, K. | |
dc.contributor.author | Taupitz, M. | |
dc.contributor.author | Wong, C.K. | |
dc.date.accessioned | 2011-02-21T09:18:30Z | |
dc.date.available | 2011-02-21T09:18:30Z | |
dc.date.issued | 2001 | |
dc.identifier.citation | Albrecht , A , Steinhofel , K , Taupitz , M & Wong , C K 2001 , ' Logarithmic simulated annealing for computer-assisted x-ray diagnosis ' , Artificial Intelligence in Medicine , vol. 22 , no. 3 , pp. 249-260 . https://doi.org/10.1016/S0933-3657(00)00112-3 | |
dc.identifier.issn | 0933-3657 | |
dc.identifier.other | dspace: 2299/5349 | |
dc.identifier.uri | http://hdl.handle.net/2299/5349 | |
dc.description | Original article can be found at http://www.sciencedirect.com Copyright Elsevier [Full text of this article is not available in the UHRA] | |
dc.description.abstract | We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. The fragments of CT images are of size 119×119 with eight bit grey levels. From 348 positive (focal liver tumours) and 348 negative examples a number of hypotheses of the type w1x1++wnxn≥ were calculated for n=14161. The threshold functions at levels two and three were determined by computational experiments. The circuit was tested on various sets of 50+50 additional positive and negative examples. For depth-three circuits, we obtained a correct classification of about 97%. The input to the algorithm is derived from the DICOM standard representation of CT images. The simulated annealing procedure employs a logarithmic cooling schedule c(k)=Γ/ ln(k+2), where Γ is a parameter that depends on the underlying configuration space. In our experiments, the parameter Γ is chosen according to estimations of the maximum escape depth from local minima of the associated energy landscape. | en |
dc.language.iso | eng | |
dc.relation.ispartof | Artificial Intelligence in Medicine | |
dc.subject | CT images | |
dc.subject | perceptron algorithm | |
dc.subject | simulated annealing | |
dc.subject | logarithmic cooling schedule | |
dc.subject | threshold functions | |
dc.subject | focal liver tumour | |
dc.title | Logarithmic simulated annealing for computer-assisted x-ray diagnosis | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | 10.1016/S0933-3657(00)00112-3 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |