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Labor induction failure prediction using Gabor filterbanks and center symmetric local binary patterns

机译:使用Gabor滤波器组和中心对称局部二值模式的人工诱导失效预测

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Labor induction is defined as the artificial stimulation of uterine contractions aimed to induce vaginal birth. Occurring in about 20% of pregnancies labor induction has become one the most commonly practiced procedures in obstetrics. One of the risk related to labor induction is cesarean section which accounts about 20% of the inductions. A requirement for a successful labor is a ripe cervix (soft and distensible). Microestructural changes occurring in the cervix during the ripening process, will affect the interaction between cervical tissues and sound waves during ultrasound transvaginal scanning and will be perceived as gray level intensity variations in the echographic image. A non-invasive method using image processing of ultrasound images may help in predicting the outcome of labor induction. In this paper a set of echography images from labor induction patients is analyzed using a multiscale methodology based on Center Symmetric Local Binary Patterns and Gabor filters. Results show that it is feasible to predict the outcome of a labor induction procedure using this method with a good accuracy.
机译:引产被定义为旨在诱发阴道分娩的子宫收缩的人工刺激。大约20%的怀孕发生引产已成为产科中最常用的方法之一。与引产有关的风险之一是剖宫产,占引产的20%。一个成功的分娩需要一个成熟的子宫颈(柔软且可扩张的)。在成熟过程中,子宫颈中发生的微结构变化将影响超声阴道扫描过程中宫颈组织和声波之间的相互作用,并且会在超声图像中被感知为灰度强度变化。使用超声图像的图像处理的非侵入性方法可以帮助预测引产的结果。在本文中,使用了基于中心对称局部二值模式和Gabor滤波器的多尺度方法,分析了来自引产患者的一系列回波描记图像。结果表明,使用这种方法以较高的准确性预测引产程序的结果是可行的。

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