Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

Abstract

International audienceUltrasound in regional anesthesia (RA) has increased in pop-ularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challeng-ing task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the me-dian nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors

Applying deep learning in recognizing the femoral nerve block region on ultrasound images

“…Computer vision (CV) is a branch of DL, which plays a crucial role in image processing. Former studies have designed different methods to achieve ultrasound image segmentation, turning out unsatisfied results (3)(4)(5). There are few studies applying deep neural network in the femoral nerve segmentation on ultrasound images.…”

Artificial intelligence in musculoskeletal ultrasound imaging

“…7 ). The method proposed by Hadjerci et al [ 56 ] uses the median binary pattern and the Gabor filter to the separated hyperechoic tissues, followed by SVM extraction of the nerve region. Furthermore, the authors reported a comparative study of nerve segmentation with a quantitative performance evaluation using 11 despeckling filters, six statistical feature extraction methods, filter- and wrapper-based feature selection, and five ML-based classifiers [ 10 ].…”

Real-time segmentation of blood vessels, nerves and bone in ultrasound-guided regional anesthesia using deep learning

“…Segmentation of nerves in ultrasound images has been studied in several publications over the last decade. Hadjerci et al [3] segmented the median nerve of the lower arm from ultrasound images using k-means clustering to find hyperechoic tissue, then a texture analysis method using a support vector machine classifier to identify the nerve. Hadjerci et al developed this method further in [4] and [5].…”