Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

Oussama Hadjerci (1) , Adel Hafiane (1) , Pascal Makris (2) , Donatello Conte (2) , Pierre Vieyres (1) , Alain Delbos (3)

1 PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique
2 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
3 Clinique Médipôle Garonne

Ultrasound 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.

Mots clés: image segmentation; feature extraction; image texture analysis; supervised learning; nerve detection

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Oussama Hadjerci, Adel Hafiane, Pascal Makris, Donatello Conte, Pierre Vieyres, et al.. Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern. 11th International Conference, ICIAR 2014, Oct 2014, Villamoura, Portugal. pp.132 - 140, ⟨10.1007/978-3-319-11755-3_15⟩. ⟨hal-01083075⟩