Detection of bone tumours in radiographic images using neural networks
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. 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 che texture, are che main features discriminating (soft) tissue from pathologic bone, the two classes most likely to be confused.
bone tumours texture self-organising feature map feature selection feed-forward neural network quality assessment