College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410200, China.
Department of Ultrasound, The Maternal and Child Health Care Hospital of Hunan Province, Changsha, Hunan, 410008, China.
Technol Health Care. 2020;28(S1):197-205. doi: 10.3233/THC-209020.
Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification.
The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed.
We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder.
Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 × 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA+SVM algorithm is better.
We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.
数字图像技术在异物检测和分类领域取得了重大进展，这对药物纯度提取和杂质分析分类有很大帮助。
冻干粉末中异物的检测和分类很重要，需要提出一种能够获得更高识别准确率的方法。
我们使用数字图像技术来检测和分类冻干粉末中的异物，并研究了图像预处理、中值滤波、维纳滤波和均值滤波平衡的过程，以更好地检测和分类冻干粉末中的异物。
通过工业小样本数据模拟，实验结果表明，在图像预处理过程中，3×3 中值滤波效果最佳。在异物识别方面，基于主成分分析（PCA）和支持向量机（SVM）算法的识别与基于 PCA 和第三近邻分类算法的识别进行了比较，结果表明 PCA+SVM 算法更好。
我们证明了可以将 PCA 和 SVM 相结合来对冻干粉末中的异物进行分类。我们提出的方法对于预测必需蛋白是有效的。
