Abstract
The identification of nerve structures is a crucial issue in the field of anesthesiology. Recently, ultrasound images have become relevant for performing Peripheral Nerve Blocking (PNB) procedures since it offers a non-invasive visualization of the nerve and the anatomical structures around it. However, the location of nerve structures from ultrasound images is a difficult task for the specialist due to the artifacts, i.e., speckle noise, which affect the intelligibility of a given image. Here, we proposed an automatic nerve structure segmentation approach from ultrasound images based on random under-sampling (RUS) and a support vector machine (SVM) classifier. In particular, we use a Graph Cuts-based technique to define a region of interest (ROI). Then, such an ROI is split into several correlated areas (superpixels) using the well-known Simple Linear Iterative Clustering algorithm. Further, a nonlinear Wavelet transform is applied to extract relevant features. Afterward, we use a classification scheme based on RUS and SVM to predict the label of each parametrized superpixel. Thus, our approach can deal with the imbalance issues when classifying a superpixel as nerve or non-nerve. Attained results on a real-world dataset demonstrate that our method outperforms similar works regarding both the dice segmentation coefficient and the geometric mean-based classification assessment.
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Acknowledgments
Under grants provided by COLCIENCIAS project 1110-744-55958: “Desarrollo de un sistema de identificación de estructuras nerviosas en imágenes de ultrasonido para la asistencia de bloqueo de nervios periféricos”. C. Jimenez is partially funded by the project E6-18-09: “Clasificador de máquinas de vectores de soporte para problemas desbalanceados con selección automática de parámetros” (Vicerrectoria de Investigaciones, Innovación y Extensión) and by Maestría en Ingeniería Eléctrica, both from Universidad Tecnológica de Pereira.
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Jimenez, C., Diaz, D., Salazar, D., Alvarez, A.M., Orozco, A., Henao, O. (2018). Nerve Structure Segmentation from Ultrasound Images Using Random Under-Sampling and an SVM Classifier. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_65
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DOI: https://doi.org/10.1007/978-3-319-93000-8_65
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