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Development of Automatic Diagnostic System of Pneumoconiosis Using Computed Radiography Chest X-ray Images

计算机X线胸部X线图像尘肺自动诊断系统的研制

基本信息

批准号：
63870043
负责人：
KOBATAKE Hidefumi
金额：
$ 11.07万
依托单位：
Tokyo University of Agriculture & Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research
财政年份：
1988
资助国家：
日本
起止时间：
1988 至 1989
项目状态：
已结题

项目摘要

The advent of increased government involvement in occupational health maintenance is requiring new approaches to medical decision making. The automatic mass diagnostic screening of medical films for the detection of a specific abnormality could one of promising approaches.This project is to develop an automatic diagnostic system of pneumoconiosis which is based on the Computed Radiography (CR). CR system is a new X-ray imaging system which produce high quality x-ray images and its equality makes it easy to develop stable automatic diagnostic system.The summary of the project is as follows: (1) More than 70 CR Tilmis of pneumoconiosis have been collected. Expert readers evaluated the profusion of opacities of pneumoconiosis of these files. From these results, pneumoconiosis CR file data base has been constructed. (2) Five fundamental methods for evaluating profusion of opacities have been developed. The correct classification rates of them ranges from 70 to 8 percent for normal/abnormal classification. (3) Synthetic diagnostic system has been developed, which is based on the methods mentioned above. Its correct classification rate for normal/abnormal decision is 90 percent. This score is obtained from partial region of chest x-ray images and then we can expect that the overall decision will be much higher.These results show than the synthetic system is promising for the realization of automatic diagnostic system. We will continue to improve each method and try to attain much higher correct decision rate.

随着政府越来越多地参与职业健康维护，医疗决策需要新的方法。通过对医用胶片的大规模自动诊断筛选来发现特定的异常可能是一种很有前途的方法之一。本项目旨在开发一种基于计算机放射成像(CR)的尘肺自动诊断系统。CR系统是一种新型的X射线成像系统，可以产生高质量的X射线图像，它的特性使得开发稳定的自动诊断系统变得容易。项目总结如下：(1)收集了70多张尘肺CR片。专家读者对这些档案中尘肺的大量混浊进行了评估。在此基础上，建立了尘肺CR档案数据库。(2)提出了评价混浊程度的五种基本方法。对于正常/异常分类，它们的正确分类率为70%至8%。(3)开发了基于上述方法的综合诊断系统。它对正常/异常决策的正确分类率为90%。从胸部X光图像的局部区域得到这一得分，可以预期总体决策会更高，这些结果表明该综合系统对于实现自动诊断系统是有前景的。我们将继续改进每一种方法，努力达到更高的正确决定率。