虽然现代医学通讯标准例如DICOM中包含了用于描述病案、患者以及成像技术等的非图像参数，但是由于这些标识(Tag)结构过简、语义交叠，甚至会由成像设备随意设置，因此并不适用于医学图像的分类。为此，提出了一个简便易行、基于树状结构的、明细检查细节的编码方案。编码由影像设备(modality)技术代码、体位(oroentation)方向代码、解剖学(anatomy)代码、生物系统(biological system)代码4个部分组成。简称为MOAB编码方案。为检验分类效果，利用该编码方案对7000余幅数字化图像建立了一套索引编制较为完善的图像数据库。通过实验和同其他编码方案的比较讨论，通过实验和同其他编码方案的比较讨论，结果证明，该编码方案能够充分描述医学图像内容。依照该编码查询和浏览图像，准确率高、速度快，为下一步开展基于医学图像内容检索的研究奠定了必备条件。

Modern communication standards, such as Digital Imaging and Communication in Medicine (DICOM), include non-image data for a standardized description of study, patient or technical parameters. However, these tags are rather roughly structured, ambiguous , and often optional. In this paper, we present a novel monohierarchical muti-axiel classification code for medical image retrieval. Our so-called MOAB coding scheme consists of four axes with three to four positions, each in {0,…,9,A,…,Z}. In particular, the modality code (M) describes imaging modality and relevant technical detail, the orientation code (O) models examined body orientation, the anatomy code (A) refers to the body region examined, and the biology code (B) describes the biological system examined. So far, the coding scheme is easily used in a medical image database of about 7000 single digital images, the loading time for 256×256 images was short, and the error rate of query result was low. The MOAB classification coding scheme enables a unique classification of medical images so as to develop further content-based medical image retrieval. The code is flexible and easily to be extended.