基于网格的x射线图像分类

Bertalya, Prihandoko, D. Kerami, T. M. Kusuma
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引用次数: 9

摘要

医学图像分类的过程仍然是利用医师或放射科医生的知识进行人工分类,导致目标识别过程不准确和缓慢。因此,我们需要一种能够准确、快速地将医学图像从查询图像分类到预定义类别的自动系统。在本研究中,我们将医学图像分类到数据库中定义的图像类别。我们着重于管理x射线图像的形状来执行分类过程,并使用欧几里得距离和杰弗里发散技术来获得图像相似度。我们使用Freeman Code来表示x射线图像的形状。本文介绍了通过简化x射线图像导体形状来获得最佳识别率的Freeman码表示方法的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of X-Ray Images Using Grid Approach
The process of medical image classification is still carried out manually using the knowledge of the physician or radiologist, which leads to inaccurate and slow process of object identification. Thus, we need an automatic system that can classify medical images, accurately and faster from query images into one of the pre-defined classes. In this research, we are dealing with the classification of medical image to the image classes that are defined in the database. We focus on managing the shape of X-ray image to perform the classification process and use the Euclidean distance and Jeffrey Divergence techniques to obtain image similarity.We use Freeman Code to represent the shape of X-ray images. This paper shows the development of the Freeman Code representation by simplifying the shape of X-ray image conducts to obtain the best recognition rate.
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