Adaptive simulated annealing for CT image classification

Article

Loomes, M., Albrecht, A., Steinhoefel, K. and Taupitz, M. 2002. Adaptive simulated annealing for CT image classification. International Journal of Pattern Recognition and Artificial Intelligence. 16 (5), pp. 573-588.
TypeArticle
TitleAdaptive simulated annealing for CT image classification
AuthorsLoomes, M., Albrecht, A., Steinhoefel, K. and Taupitz, M.
Abstract

This paper presents a pattern classification method that combines the classical Perceptron algorithm with simulated annealing. The approach is applied to the recognition of focal liver tumors presented in the DICOM format. On test sets of 100+100 examples (disjoint from the learning set) we obtain a correct classification of more than 98%.
This work was carried out as part of a collaboration with medical practitioners based at the Institute of Radiology, Humboldt University of Berlin. This paper builds upon work first presented at ESANN 2001.

Research GroupSensoLab group
PublisherWorld Scientific Publishing Co. Pte Ltd
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
ISSN0218-0014
Publication dates
PrintAug 2002
Publication process dates
Deposited17 Oct 2008
Output statusPublished
Web address (URL)http://www.worldscinet.com/cgi-bin/details.cgi?id=pii:S0218001402001848&type=html
LanguageEnglish
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