Abstract
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.
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Notes
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- 2.
Dim denotes the image grid dimension. In this work, we used \(\dim =2\) for 2D images.
- 3.
We use the BSM implementation available on https://code.google.com/p/asmlib-opencv/.
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Acknowledgments
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.
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García, H.F., Giraldo, J.J., Álvarez, M.A., Orozco, Á.A., Salazar, D. (2015). Peripheral Nerve Segmentation Using Speckle Removal and Bayesian Shape Models. 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_44
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