Automatic identification of needle insertion site in epidural anesthesia with a cascading classifier

Abstract

Ultrasound imaging was used to detect the anatomic structure of lumbar spine from the transverse view, to facilitate needle insertion in epidural anesthesia. The interspinous images that represent proper needle insertion sites were identified automatically with image processing and pattern recognition techniques. On the basis of ultrasound video streams obtained in pregnant patients, the image processing and identification procedure in a previous work was tested and improved. The test results indicate that the pre-processing algorithm performs well on lumbar spine ultrasound images, whereas the classifier is not flexible enough for pregnant patients. To improve the accuracy of identification, we propose a cascading classifier that successfully located the proper needle insertion site on all of the 36 video streams collected from pregnant patients. The results indicate that the proposed image identification procedure is able to identify the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists' work to identify the needle insertion point precisely and effectively.

Keywords: Automatic identification; Cascading classifier; Epidural anesthesia; Local normalization; Medical image processing; Pattern recognition; Template matching; Ultrasound imaging guidance; Video processing.