Abstract
Regional anesthesia is carried out using a technique called peripheral nerve blocking (PNB), which involves the administration of an anesthetic nearby the nerve. Ultrasound images have been widely used for PNB procedure due to their low cost and because they are non-invasive. However, the segmentation of nerve structures in ultrasound images is a challenging task for the specialists since the images are affected by echo perturbations and speckle noise. Automatic or semi-automatic segmentation systems can be developed in order to aid the specialist for locating nerves structures accurately. In this paper we propose a methodology for the semi-automatic segmentation of nerve structures in ultrasound images. We use non-linear Wavelets transform in the feature extraction step and for the classification stage we use a Gaussian Processes classifier. Experimental results show that the implemented methodology can segment nerve structures accurately.
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For the Gaussian Processes classifier, we use the software available on http://www.gaussianprocess.org/gpml/code/matlab/doc/.
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Acknowledgment
This work was developed under the project “Desarrollo de una metodología para la segmentación automática de regiones objetivo en imágenes ultrasónicas a partir de modelos estadísticos. Aplicación a los procedimientos de anestesia regional”, with financial support of the Universidad Tecnológica de Pereira. Furthermore we want to thank the Dr. Diego Salazar from Confamiliar Clinic, who labeled the nerve structures and helped us to acquire the ultrasound images.
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González, J.G., Álvarez, M.A., Orozco, Á.A. (2015). Peripheral Nerves Segmentation in Ultrasound Images Using Non-linear Wavelets and Gaussian Processes. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_68
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DOI: https://doi.org/10.1007/978-3-319-19390-8_68
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