Peripheral Nerve Segmentation Using Speckle Removal and Bayesian Shape Models
In the field of medicine, ultrasound images have become a useful tool for visualizing nerve structures in the process of anesthesiology. Although, these images are commonly used in medical procedures such as peripheral nerve blocks. Their poor intelligibility makes it difficult for the anesthesiologists to perform this process accurately. Therefore, an automated segmentation methodology of the peripheral nerves can assist the experts in improving accuracy. This paper proposes a peripheral nerve segmentation method in medical ultrasound images, based on Speckle removal and Bayesian shape models. The method allows segmenting efficiently a given nerve by performing a Bayesian shape fitting. The experimental results show that performing a speckle removal before fitting the model, improves the accuracy due to the enhancement of the image to segment.
KeywordsPeripheral nerve segmentaion Speckle removal Bayesian shape models
This research was developed under the project financed by the Pereira Technological University with code CIE 6-13-6. H.F. García is funded by Colciencias under the program: formación de alto nivel para la ciencia, la tecnología y la innovación - Convocatoria 617 de 2013.
- 4.Rueda, S., Fathima, S., Knight, C., Yaqub, M., Papageorghiou, A., Rahmatullah, B., Foi, A., Maggioni, M., Pepe, A., Tohka, J.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33, 797–813 (2013)CrossRefGoogle Scholar
- 5.Peng, Z., Wee, W., Lee, J.H.: Automatic segmentation of mr brain images using spatial-varying gaussian mixture and markov random field approach. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 80, June 2006Google Scholar
- 7.Zhou, Y., Gu, L., Zhang, H.J.: Bayesian tangent shape model: estimating shape and pose parameters via bayesian inference. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003, pp. 109–116 (2003)Google Scholar
- 9.Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision (2004)Google Scholar