Zhou Liyuan, Gao Hongmei, Gao Dingguo, Zhao Qijun
School of Information Science and Technology, Tibet University, Lhasa 850011, China.
Entropy (Basel). 2023 May 25;25(6):847. doi: 10.3390/e25060847.
Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.
藏药材在藏文化中发挥着重要作用。然而，有些种类的藏药材形状和颜色相似，但药用特性和功能却不同。错误使用这类药材可能导致中毒、延误治疗，并给患者带来潜在的严重后果。历史上，椭圆状草本藏药材的鉴别依赖于人工鉴别方法，包括观察、触摸、品尝和闻气味，这严重依赖技术人员积累的经验，且容易出错。在本文中，我们提出了一种结合纹理特征提取和深度学习网络的椭圆状草本藏药材图像识别方法。我们创建了一个由18种椭圆状藏药材的3200张图像组成的图像数据集。由于图像中椭圆状草本藏药材的背景复杂，形状和颜色相似度高，我们对这些药材的形状、颜色和纹理特征进行了多特征融合实验。为了突出纹理特征的重要性，我们利用改进的局部二值模式（LBP）算法对由伽柏（Gabor）算法提取的纹理特征进行编码。我们将最终特征输入到密集连接网络（DenseNet）中，以识别椭圆状草本藏药材的图像。我们的方法专注于提取重要的纹理信息，同时忽略诸如背景杂波等无关信息，以消除干扰并提高识别性能。实验结果表明，我们提出的方法在原始数据集上的识别准确率为93.67%，在扩充数据集上为95.11%。总之，我们提出的方法有助于椭圆状草本藏药材的鉴别和认证，减少错误，并确保藏药材在医疗保健中的安全使用。
