Pre-diagnosis of pelvic floor disorders-based image registration and clustering

Cicero L. Costa, Túlia A. A. Macedo, C. Barcelos
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Abstract

Pelvic dysfunction mainly affects adult women, it is estimated that 15% of multiparous women suffer from the problem. Dysfunctions can be diagnosed by defecography, a dynamic MRI scan. Images are used by specialists to diagnose organ dysfunction such as the bladder and the early rectum. This paper presents an automated classification system that uses a non-rigid registration based on a variational model to create automatic markings from initial markings made by an expert. The classification is based on simple average and the centroids of the K-means grouping technique. The classification made by the system is evaluated by confusion matrix based metrics. The obtained results using 21 defecography exams from 21 different patients indicate that the proposed technique is a promising tool in the diagnosis of pelvic floor disorders and can assist the physician in the diagnostic process.
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基于图像配准和聚类的盆底疾病预诊断
骨盆功能障碍主要影响成年女性,估计有15%的多胎妇女患有该问题。功能障碍可以通过排便造影诊断,这是一种动态MRI扫描。影像被专家用来诊断器官功能障碍,如膀胱和早期直肠。本文提出了一种自动分类系统,该系统使用基于变分模型的非刚性配准,从专家所做的初始标记中创建自动标记。分类是基于简单平均和k均值分组技术的质心。通过基于混淆矩阵的度量来评估系统所做的分类。对21例不同患者的21例排便造影检查结果表明,所提出的技术是诊断盆底疾病的一种很有前途的工具,可以帮助医生进行诊断。
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