Development of intelligent image database system for medical x-ray images

本研究试图通过集图像理解、计算机辅助诊断、图像检索于一体，开发一个高度智能化的医用X光图像数据库。(1)移植过去的系统：我们已经开发了一个唯一的胸部X光图像数据库，该数据库具有对存储在数据库中的图像进行草图操作的能力。通过这个系统，我们可以通过使用草图上给出的图形键和逐个图例查询方案来检索所需的图像。由于该系统是在传统的大型计算机上实现的，我们首先将该系统移植到EWS(SUN-4)上，并增加了将诊断记录写入原始X射线图像的新功能。(2)X射线图像的计算机辅助诊断：应用图像理解技术对肺癌和胃癌的计算机诊断进行了研究。对于胃癌，我们开发了一种程序，从双对比胃X光图像中检测出疑似异常的小区域。对一种用于大批量筛选的100 mm胶片进行了自动筛选系统的测试，明确了该系统的局限性和今后改进的基本方针。对于尘肺的定量诊断，我们开发了一种程序，通过检测尘肺X光图像的肺野内观察到的微小圆形阴影，将疾病自动分类为正常和九类。(3)辅助软件工具：为了支持计算机辅助图像诊断系统的开发，我们改进了之前开发的图像处理专家系统IMECT，并开始研究智能图像界面。对于前者，我们在系统印象中增加了目标变换函数。

In this research, we tried to develope a highly intelligent image database for medical x-ray images , by integrating image understanding computer-aided diagnosis, and image retrieval.(1)Transferring the past system : We already developed a unique database for chest x-ray images which has the ability to manipulate sketches of the images stored in the database. By this system we can retrieve desired images by using pictorial keys given on a sketch and <query-by-pictorial-example> schemes. Since this system was implemented on a conventional large scale computer, we first transferred this system to the EWS(SUN-4) , and added the new function to write down diagnostic records into an original x-ray image.(2)Computer-aided diagnosis of cancer by x-ray images : We studied comuter diagnosis of lung and stomach cancer by applying image understanding techniques. Concerning stomach cancer we developed a procedure to detect small regions which are suspected to the abnormal from a double-contrast stomach x-ray image. Automated screening system for a photofluorogram was tested its performance to diagnose a 100mm films now used for mass screening and its limitations and basic policy for future improvement were made clear. For quantitative diagnosis of pneumoconiosis, we developed a procedure to automatically classify diseases into normal and nine classes according to the criteria given by ILO by detecting small rounded opacities observed in the lung field of pneumoconiosis x-ray images.(3)Supporting software tool : In order to support development of computer-aided image diagnosis system, we improved the image processing expert system IMPRESS we had previously developed , and began to study the intelligent image interface. Concerning the former, we added a goal transformation function to the system IMPRESS.