IEEE J Biomed Health Inform. 2014 Jul;18(4):1370-8. doi: 10.1109/JBHI.2014.2302437.
Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.
盆腔器官脱垂（POP）是一个主要的女性健康问题。由于目前临床检查的不准确性，通过磁共振成像（MRI）对其进行诊断已经变得流行起来。MRI 上 POP 的诊断包括确定骨盆骨结构上的参考点进行测量和评估。然而，目前这是手动进行的，因此是一个耗时且主观的过程。我们提出了一种新的分割方法，用于自动识别 MRI 上的骨盆骨点。它由基于纹理的块分类、泄漏检测和先验形状信息的多阶段机制组成。基于纹理的块分类和 K-均值算法的聚类分析被集成在一起，以生成初始骨骼分割并识别泄漏区域。先验形状信息被合并以获得最终的骨骼分割。然后，使用形态学骨架操作来识别参考点。结果表明，与其他分割方法相比，所提出的方法实现了更高的骨骼分割准确性。与专家手动识别点的过程相比，该方法还可以更快且更一致地自动识别参考点。这项研究旨在实现更快和更一致的 MRI 盆腔测量，以促进和改善女性 POP 的诊断。
