Nowadays, prediction of abnormality plays a vital role in healthcare applications for deciding the treatment and guiding for proper treatment on time. The amniotic fluid is the water of the womb, and it is a strong indicator of congenital fetal anomaly. The automatic calculation of amniotic fluid index (AFI) and shape features of varying gestational periods will be useful to predict the perinatal outcome of high risk in maternity patients. Some perinatal outcomes are expected fetal weight, head circumferences and need of new-born ICU which decide the mode of delivery. These perinatal outcomes will be helpful in increasing the live birth and reducing the risk of premature delivery. The aim of this work is to identify the abnormal AFI of expectant mothers to alert the clinicians. Computer-aided diagnosis supports the clinicians in decision-making process. In the proposed work, using the training set of ultrasound images, the shape templates are developed by using deformable methods. Contour points in the edges will be helpful to find the AFI. After that, features are extracted and fuzzy logic algorithm is used to classify the given image into one of the four categories such as oligohydramnios, borderline, normal and hydramnios state for expectant mothers and their impact on fetal growth. The outcome of the proposed approach is measured in two different ways. The first outcome is that calculated AFI will be compared with the value calculated by the radiologist/clinicians, and the second outcome is that along with AFI, shape feature with contour points and gestational age are used for making decision/classification such as normal, borderline, oligohydramnios and hydramnios, and the classified results will also be compared with the expert’s opinion. The outcomes are represented quantitatively. The results proved that AFI calculated by the proposed work was matching 94% with the expert opinion and classification of test image into any one of the categories such as normal, borderline, oligohydramnios and hydramnios fetched average accuracy of prediction up to 92.5%.

中文翻译：

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使用 AFI 预测胎儿安全和生长的模糊方法的开发

如今，异常预测在医疗保健应用中对于决定治疗方法和指导及时正确治疗起着至关重要的作用。羊水是子宫内的水，是胎儿先天性异常的有力指标。羊水指数（AFI）和不同孕期形状特征的自动计算将有助于预测产妇高危患者的围产期结局。一些围产期结局是预期的胎儿体重、头围和新生儿重症监护病房的需要，这决定了分娩方式。这些围产期结果将有助于增加活产率和降低早产风险。这项工作的目的是识别孕妇的异常 AFI 以提醒临床医生。计算机辅助诊断支持临床医生的决策过程。在所提出的工作中，使用超声图像的训练集，通过使用可变形方法开发形状模板。边缘的轮廓点将有助于找到 AFI。之后，提取特征并使用模糊逻辑算法将给定的图像分类为孕妇羊水过少、边缘、正常和羊水过多状态及其对胎儿生长的影响等四类之一。拟议方法的结果以两种不同的方式衡量。第一个结果是计算出的 AFI 将与放射科医生/临床医生计算的值进行比较，第二个结果是与 AFI 一起，具有轮廓点和胎龄的形状特征用于做出正常、边缘、羊水过少和羊水过多等决策/分类，并将分类结果与专家的意见进行比较。结果是定量表示的。结果证明，所提出的工作计算的 AFI 与专家意见匹配 94%，并将测试图像分类为正常、边缘、羊水过少和羊水过多等任一类别，平均预测准确率高达 92.5%。