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Detection of Bone Tumours in Radiographic Images using Neural Networks (1999)
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BibTeX
@MISC{Egmont-Petersen99detectionof,
author = {Michael Egmont-Petersen and Erich Pelikan and E. Pelikan},
title = {Detection of Bone Tumours in Radiographic Images using Neural Networks},
year = {1999}
}
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Abstract
We develop an approach for segmenting radiographic images of focal bone lesions possibly caused by bone tumour. A neural network is used to classify individual pixels by a convolution operation based on a feature vector. We design eight features chat characterise the local texture in the neighbourhood of a pixel. Four of the features are based on co occurrence matrices computed from the neighbourhood. The true class label of the pixels in the radiographs are obtained from annotations made by an experienced radiologist. We make a comparison of several statistical classifiers based on different criteria and argue that neural networks are most suited for our application. Feed-forward neural networks and self-organising feature maps are trained to perform the segmentation cask. The experiments confirm che feasibility of using a feature-based neural network for finding pathologic bone changes in radiographic images. An analysis of the eight features indicates that the presence of edges and transitions, the complexity of the texture, as well as the amount of high frequencies in the texture, are the main features discriminating (soft) tissue from pathologic bone, the two classes most likely to be confused.
Keyphrases
neural network radiographic image bone tumour co occurrence matrix convolution operation individual pixel pathologic bone change feed-forward neural network pathologic bone segmentation cask experienced radiologist high frequency true class label different criterion focal bone lesion che feasibility feature vector local texture feature-based neural network main feature several statistical classifier self-organising feature map
