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 that 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. Neural networks and self-organising feature maps are trained to perform the segmentation task. The experiments confirm the 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.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Receiveed: 4 June 1998¶,Received in revised form: 15 September 1999¶Accepted: 11 December 1998
Rights and permissions
About this article
Cite this article
Egmont-Petersen, M., Pelikan, E. Detection of Bone Tumours in Radiographic Images using Neural Networks. Pattern Analysis & Applications 2, 172–183 (1999). https://doi.org/10.1007/s100440050026
Published:
Issue Date:
DOI: https://doi.org/10.1007/s100440050026