The purpose of this paper is to help the anesthesiologist to find the
epidural depth automatically to make the first attempt to enter the path of the
needle into the patient's body while it is clogged with bone and avoid causing
a puncture in the surrounding areas of the patient`s back. In this regard, a
morphology-based bone enhancement and detection followed by a
Ramer-Douglas-Peucker algorithm and Hough transform is proposed. The proposed
algorithm is tested on synthetic and real ultrasound images of laminar bone,
and the results are compared with the template matching based Ligamentum Flavum
(LF) detection method. Results indicate that the proposed method can faster
detect the diagonal shape of the laminar bone and its corresponding epidural
depth. Furthermore, the proposed method is reliable enough providing
anesthesiologists with real-time information while an epidural needle insertion
is performed. It has to be noted that using the ultrasound images is to help
anesthesiologists to perform the blind injection, and due to quite a lot of
errors occurred in ultrasound-imaging-based methods, these methods can not
completely replace the tissue pressure-based method. And in the end, when the
needle is injected into the area (dura space) measurements can only be trusted
to the extent of tissue resistance. Despite the fairly limited amount of
training data available in this study, a significant improvement of the
segmentation speed of lumbar bones and epidural depth in ultrasound scans with
a rational accuracy compared to the LF-based detection method was found.

为帮助麻醉专家在患者体内使用针头进入针路的初始尝试时，自动找到被骨骼堵塞的硬膜外深度，并避免刺破患者背部周围区域，此论文提出了一种基于形态学骨骼增强与检测的算法，接着使用 Ramer-Douglas-Peucker 算法和 Hough 变换。该算法在合成和真实的超声图像上进行了测试，并与基于模板匹配的黄韧带（LF）检测方法进行了比较。结果表明，该方法能更快地检测到硬膜外深度对应的骨骼的对角线形状，提供麻醉专家在硬膜外穿刺时实时信息。然而，使用超声图像的方法仍然存在一定的误差，不能完全取代基于组织压力的方法。虽然本研究中的训练数据量相对较少，但在超声扫描中实现了腰椎骨骼和硬膜外深度的分割速度显著提高，准确性也较基于 LF 检测方法有所改善。