Segmentation of dried amnion biomaterial tends to produce invalid (outlier) contour point detections due to texture and colour inhomogeneity of the biomaterial. In this paper, a novel implementation of a non-stationary Bayesian learning process for outlier contour point removal of amnion segmentations is presented. This outlier removal method is independent to algorithms used for the contour detection. The Bayesian process uses a non-stationary kernel to learn a function with complex shape that maps image features in a region-of-interest around each contour point to a discrete output. Based on this output, a contour point can be determined as valid or invalid (outlier). The hyper-parameters of the non-stationary kernel are learned by maximising the marginal likelihood of the combined likelihood of data and the prior of the kernel parameters. Moreover, a novel combination of gradient-ascend and harmonic heuristic search methods is presented to find the optimal hyper-parameters. To validate the method, experiments are conducted to detect and ignore invalid contour points on amnion biomaterial images. A comparison of the proposed method with a logistic regression classification as the baseline is performed. The results show that the proposed method can significantly improve the contour detection by removing outliers and, hence, can reduce waste of uncut biomaterials.

由于生物材料的质地和颜色不均匀，干燥羊膜生物材料的分割往往会产生无效的（异常值）轮廓点检测。在本文中，提出了一种用于去除羊膜分割的异常轮廓点的非平稳贝叶斯学习过程的新实现。这种异常值去除方法与用于轮廓检测的算法无关。贝叶斯过程使用非平稳内核来学习具有复杂形状的函数，该函数将每个轮廓点周围感兴趣区域中的图像特征映射到离散输出。基于这个输出，轮廓点可以被确定为有效或无效（异常值）。非平稳核的超参数是通过最大化数据的组合似然和核参数的先验的边际似然来学习的。此外，提出了梯度上升和谐波启发式搜索方法的新颖组合，以找到最佳超参数。为了验证该方法，进行了实验以检测和忽略羊膜生物材料图像上的无效轮廓点。将所提出的方法与作为基线的逻辑回归分类进行比较。结果表明，所提出的方法可以通过去除异常值显着改善轮廓检测，从而减少未切割生物材料的浪费。将所提出的方法与作为基线的逻辑回归分类进行比较。结果表明，所提出的方法可以通过去除异常值显着改善轮廓检测，从而减少未切割生物材料的浪费。将所提出的方法与作为基线的逻辑回归分类进行比较。结果表明，所提出的方法可以通过去除异常值显着改善轮廓检测，从而减少未切割生物材料的浪费。