论文标题
使用共发生矩阵中能量变化的医疗和检查系统中的表面异常检测
Surface abnormality detection in medical and inspection systems using energy variations in co-occurrence matrixes
论文作者
论文摘要
表面缺陷的检测是图像处理和机器视觉领域中最重要的问题之一。在本文中，提出了一种基于共发生矩阵的能量变化来检测表面缺陷的方法。提出的方法包括训练和测试的两个阶段。在训练阶段，首先将共发生矩阵算子应用于健康图像，然后计算出输出能量的量。在下文中，根据能量的变化，定义了合适的特征向量，并在其帮助下，获得了图像健康的合适阈值。然后，在测试阶段，借助计算出的法定人数，有缺陷的部分与健康的部分区分开。在结果部分中，提到的方法已应用于石材和陶瓷图像上，并且已经计算出其检测精度并与某些先前方法进行了比较。在提出方法的优点中，由于使用训练阶段，我们可以提及高精度，低计算和与所有类型水平的兼容性。所提出的方法可用于医疗应用中，以检测异常（例如疾病）。因此，在2D-HELA数据集上评估了性能以对细胞表型进行分类。所提出的方法可在2D-螺旋上提供约89.56％的精度。
Detection of surface defects is one of the most important issues in the field of image processing and machine vision. In this article, a method for detecting surface defects based on energy changes in co-occurrence matrices is presented. The presented method consists of two stages of training and testing. In the training phase, the co-occurrence matrix operator is first applied on healthy images and then the amount of output energy is calculated. In the following, according to the changes in the amount of energy, a suitable feature vector is defined, and with the help of it, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective parts are distinguished from the healthy ones. In the results section, the mentioned method has been applied on stone and ceramic images and its detection accuracy has been calculated and compared with some previous methods. Among the advantages of the presented method, we can mention high accuracy, low calculations and compatibility with all types of levels due to the use of the training stage. The proposed approach can be used in medical applications to detect abnormalities such as diseases. So, the performance is evaluated on 2d-hela dataset to classify cell phenotypes. The proposed approach provides about 89.56 percent accuracy on 2d-hela.
