Ionescu Mihaela, Glodeanu Adina Dorina, Marinescu Iulia Roxana, Ionescu Alin Gabriel, Vere Cristin Constantin
Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Romania.
Department of Internal Medicine, University of Medicine and Pharmacy of Craiova, Romania.
Curr Health Sci J. 2022 Apr-Jun;48(2):196-202. doi: 10.12865/CHSJ.48.02.09. Epub 2022 Jun 30.
Medical databases usually contain a significant volume of images, therefore search engines based on low-level features frequently used to retrieve similar images are necessary for a fast operation. Color, texture, and shape are the most common features used to characterize an image, however extracting the proper features for image retrievals in a similar manner with the human cognition remains a constant challenge. These algorithms work by sorting the images based on a similarity index that defines how different two or more images are, and histograms are one of the most employed methods for image comparison. In this paper, we have extended the concept of image database to the set of frames acquired following wireless capsule endoscopy (from a unique patient). Then, we have used color and texture histograms to identify very similar images (considered duplicates) and removed one of them for each pair of two successive frames. The volume reduction represented an average of 20% from the initial data set, only by removing frames with very similar informational content.
医学数据库通常包含大量图像，因此基于常用于检索相似图像的低级特征的搜索引擎对于快速操作是必要的。颜色、纹理和形状是用于表征图像的最常见特征，然而，以与人类认知相似的方式提取用于图像检索的适当特征仍然是一个持续的挑战。这些算法通过基于定义两个或多个图像差异程度的相似性指数对图像进行排序来工作，直方图是图像比较中最常用的方法之一。在本文中，我们将图像数据库的概念扩展到了无线胶囊内窥镜检查后获取的帧集（来自唯一患者）。然后，我们使用颜色和纹理直方图来识别非常相似的图像（视为重复图像），并为每对连续的两帧移除其中之一。仅通过移除具有非常相似信息内容的帧，体积减少量平均占初始数据集的20%。
