Pixel Classification Based on Local Gray Level Rectangle Window Sampling for Amniotic Fluid Segmentation

This study analyses the use of a pixel classification model to segment amniotic fluid areas on ultrasound (US) images characterized by noise, blurry edge, artifacts, and low contrast. In contrast with the previous methods, this study constrains a training set of pixels based on neighbourhood information with the rectangle window sampling method used to determine the characteristics of each pixel in its environment specifically. The feature extraction is no longer based on the global characteristics of the object rather by taking the value of each pixel in the object area using the sampling window. This research also combines the local first-order statistical methods and gray level information in the window to obtain the characteristics of each pixel. Furthermore, Random Forest and Decision Tree (C.45) were used to classify each pixel into four classes, namely amniotic fluid, placenta, uterus, and fetal body. The classification performance testing of pixel sampling data showed that the Random forest with 5 × 7 window sizes achieved the highest performance at 99.5% accuracy, precision, and recall, respectively. Furthermore, the proposed model was evaluated using 50 new test US images to segment the amniotic fluid area. According to experimental result, proposed models can produce better segmentation area with an increase in the IoU value by 18.3% or a Jaccard coefficient value rate of 0.183 in the range of 0-1 with the previous state of the art method. Furthermore, the proposed model reduces the error rate and improves accuracy by 6.61% and 84.77%, respectively.