DEVELOPMENT OF COMPUTER-AIDED DIAGNOSTIC SYSTEM USING X-RAY IMAGE FEACHER ANALYSIS

利用 X 射线图像 FEAHER 分析的计算机辅助诊断系统的开发

基本信息

批准号：
04454503
负责人：
WADA Takuro
金额：
$ 0.77万
依托单位：
HIROSHIMAUNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

项目摘要

In this study, some restration methods for the x -ray images with optical blurs and quantum mottles were developed by considering each physical mechanism of the medical x-ray image formation. More specifically, the optical blurs were first deterministically cleared away by using the transfer characteristic of the laser scanner, the characteristic curve of the film, the logarithmic transformation of the optical density and a pseud-invers filter based on the point spread function. Next, two restoration methods for remaining quantum mottles were derived from the basis of a Bayrs' theorem. One was a wide sensive digital filter of a stochastic type using the statistical properties of quantum mottles. Another was a new extended regression model that might have a little information lack and a simple experimental regression model. Thus, the quantum mottles were statistically removed. A computer-aided diagnostic (CAD) system using these filters is developed and applied for oral radiology. Denti … More gerous cyst, odontogenic keratocyst and ameloblastoma in maxillofacial region occur frequently, but their recuperations are diffrent. Then it is necessary to make an exact differential diagnosis, although their symptoms are similar to one another. As a pilot study, we investigated statistical methods using a logistic regression and neural network. Under these studies, we presented as "Differential diagnosis by neural network and statistical analysis in oral radiology" in the 12th Annual eeting Japanese Association of Medical Physics in July 1995. And, we also presented as "Radiographic differential diagnosis among dentigerous cyst, odontogenic keratocyst and ameloblastma using statistical methods and neural networrk" in the 36th Japanese Society for Oral and Maxillofacial Radiology Annual meeting in September 1995. Based on these results, in 1996, we constructed the system and accumulated data. Kodera presented as "Radiographic differentiial diagnosis in dental region using expert system" in teh 37th Japanese Society for Oral and Maxillofacial Radiology Annual meeting in September 1996. Finally, we evaluated clinically the developed system in HIroshima University, namely dentists evaluated and confirmed the deffectiveness of these methods. The method proposed in this study was applied to the other area in dental images. Less

在这项研究中，通过考虑医疗X射线图像形成的每种物理机制，开发了具有光学斑点和量子电动机的X射线图像的一些恢复方法。更具体地说，首先使用激光扫描仪的转移特性，膜的特征曲线，光密度的对数转换和基于点扩散函数的伪内滤波器，从而确定光束被确定清除。接下来，剩余的量子电动机的两种恢复方法源自Bayrs定理的基础。一种是使用量子电动机的统计特性的随机类型的广泛敏感数字滤波器。另一个是一个新的扩展回归模型，可能缺乏信息，并且一个简单的实验回归模型。那就是统计去除量子电动机。开发并应用了使用这些过滤器的计算机AID诊断系统（CAD）系统。牙本质…颌面区域中较轻的囊肿，牙源性角膜细胞和绿细胞瘤经常发生，但它们的恢复是不同的。然后，有必要进行确切的鉴别诊断，尽管它们的症状相似。作为一项试点研究，我们使用逻辑回归和神经元网络研究了统计方法。在这些研究中，我们在1995年7月的第12届医学物理学年度会议中作为“通过神经网络和口腔放射学中的统计分析进行了差异诊断”。我们也称为“牙科囊肿，牙源性疾病囊肿，牙源性角化细胞和阿莫尔布拉斯特术的牙科囊肿，牙源性囊肿和神经网络学的统计学上的日本和神经群体”的“放射线差异诊断”。 1995。根据这些结果，1996年，我们构建了系统并累积数据。 Kodera在1996年9月的第37届日本口服和颌面放射学会年会上作为“使用专家系统的牙科区域的放射鉴别诊断”。最后，我们评估了Hiroshima University在Hiroshima University的临床上评估了牙科医生的临床系统，并确认了这些方法的缺陷。本研究中提出的方法应用于牙科图像中的其他区域。较少的