Nerve Structure Segmentation from Ultrasound Images Using Random Under-Sampling and an SVM Classifier

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.