Neural network texture segmentation in equine leg ultrasound images

We propose a texture segmentation method based on frequency characteristics in a hybrid neural network approach using both unsupervised and supervised neural network classifiers. Our goal is to segment out tendons accurately and repeatedly from clinical ultrasound (US) images of horse tendons. The proposed method first extracts frequency-based texture features through the discrete cosine transform (DCT). A self-organizing-map (SOM) neural network is used for unsupervised classification. Following unsupervised training, a supervised neural network, learning vector quantization (LVQ), is used to improve further the performance and accuracy of segmentation. In terms of efficiency, only rotationally invariant features are adopted. The experimental results show that improvements can also be achieved by a feature selection scheme. The experimental images were all captured at a veterinary hospital. The results favourably compare to gold standards created by a radiologist.