Computer-assisted diagnosis of focal liver lesions on CT images: evaluation of the perceptron algorithm

Hein, Eike and Albrecht, Andreas A. and Melzer, Daniela and Steinhofel, Kathleen and Rogalla, Patrick and Hamm, Bernd and Taupitz, Matthias (2005) Computer-assisted diagnosis of focal liver lesions on CT images: evaluation of the perceptron algorithm. Academic Radiology, 12 (9). 1205 -1210. ISSN 1076-6332

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Abstract

Rationale and Objective:
The purpose of the study was to investigate a modified version of a so-called Perceptron algorithm in detecting focal liver lesions on CT scans.

Materials and Methods:
The modified Perceptron algorithm is based on simulated annealing with a logarithmic cooling schedule and was implemented on a standard workstation. The algorithm was trained with 400 normal and 400 pathologic CT scans of the liver. An additional 100 normal and 100 pathologic scans were then used to test the detection of pathology by the algorithm. The total of 1000 scans used in the study were selected from the portal venous phase of upper abdominal CT examinations performed in patients with normal findings or hypovascularized liver lesions. The pathologic scans contained 1 to 4 focal liver lesions. For the preliminary version of the algorithm used in this study, it was necessary to define regions of interest that were converted to a matrix of 119 x 119.

Results:
Training of the algorithm with 400 examples each of normal and abnormal findings took about 75 hours. Subsequently, the testing took several seconds for processing each scan. The diagnostic accuracy in discriminating scans with and without focal liver lesions achieved for the 200 test scans was approximately 99%. The error rate for pathologic and normal scans was comparable to results reported in the literature, which, however, were obtained for much smaller test sets.

Conclusion:
The modified Perceptron algorithm has an accuracy of close to 99% in detecting pathology on CT scans of the liver showing either normal findings or hypovascularized focal liver lesions.

Item Type: Article
Keywords (uncontrolled): Pattern recognition, CT, CAD, neoplasm
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 12384
Useful Links:
Depositing User: Andreas Albrecht
Date Deposited: 08 Nov 2013 07:00
Last Modified: 13 Oct 2016 14:29
URI: http://eprints.mdx.ac.uk/id/eprint/12384

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