Fracture detection and quantitative measure of displacement in pelvic CT images

Jie Wu, Pavani Davuluri, Ashwin Belle, Charles Cockrell, Yang Tang, Kevin Ward, R. Hobson, K. Najarian
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引用次数: 7

Abstract

Traumatic pelvic injury is a severe and common injury in the United States. The automatic detection of fractures in pelvic CT images is a significant contribution for assisting physicians in making faster and more accurate patient diagnostic decisions and treatment planning. However, due to the low resolution and quality of the original images, the complexity of pelvic structures, and the difference in visual characteristics of fracture by their location, it is difficult to detect and accurately locate the pelvic fractures and determine the severity of the injury. In this paper, an automatic hierarchical algorithm for detecting pelvic bone fractures in CT scans is proposed. The algorithm utilizes symmetric comparison, adaptive windowing, boundary tracing, wavelet transform. Also, the quantitative measure of fracture severity in pelvic CT scans is defined. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, shows potential for clinical application. Statistical results also indicate the superiority of the proposed method.
骨盆CT图像的骨折检测与位移定量测量
在美国,创伤性骨盆损伤是一种严重而常见的损伤。骨盆CT图像中骨折的自动检测对于帮助医生做出更快、更准确的患者诊断决策和治疗计划有着重要的贡献。然而,由于原始图像的分辨率和质量较低,骨盆结构的复杂性,以及骨折的视觉特征因其位置的不同而存在差异,因此很难检测和准确定位骨盆骨折并确定损伤的严重程度。本文提出了一种基于CT扫描的骨盆骨折自动分层检测算法。该算法利用对称比较、自适应加窗、边界跟踪和小波变换。此外,还定义了骨盆CT扫描中骨折严重程度的定量测量。结果表明,该方法能够准确地自动检测出大骨折和小骨折,具有临床应用潜力。统计结果也表明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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