Basic study of computer diagnosis of high precision chest x-ray images Using digital radiography

(1)本项目的首要目标是改进我们开发的ascr - v3（胸片自动判读系统-第3版）的性能，使其有足够的能力处理最新数字放射摄影（DR）拍摄的高质量图像。我们将系统可以处理的图像大小从300 × 300像素扩展到600 × 600像素。我们开发了一种新的程序来识别血管阴影使用位置变化对比过滤器，并提高了检测肺异常阴影的能力。(2)尘肺病的计算机诊断。我们新开发了一种程序来识别肺中的小圆形混浊物并测量这些混浊物的密度，通过采用差异类型过滤，然后是模式分类算法。通过将一组相同类型的过滤器与不同尺寸的口罩组合在一起，我们推导出一种统一执行尘肺病密度分类和尺寸分类的程序。将该方法应用于国际劳工组织（ILO）国际标准胶片装置，取得了良好的效果。此外，我们还尝试了从尘肺x线CT图像中识别圆形混浊物，并证明了其可行性。(3)将两种二元操作两幅图像应用于胸部x线图像的计算机诊断。一种是通过能量减法DR图像检测肺癌。这揭示了能量减法图像的特点和计算机解译能量减法图像的可行性。另一种是自动检测和测量同一患者在不同时间拍摄的图像序列之间的变化量。我们开发了一个由仿射变换配准和局部互相关变化检测组成的程序。并将其应用于肺癌诊断。

(1) Our first aim in this project is to improve the performance of AISCR-V3(Automated Interpretation System of Chest Radiograms - Version 3) which we developed, so that it may have enough ability to treat high quality images taken by recent digital radiography (DR). We extended the picture size the system can process from 300 x 300 pixels to 600 x 600 pixels. We developed a new procedure to recognize vessel shadows using a position variant contast filter, and improved the ability to detect abnormal shfdows in the lung.(2) Concerning computer diagnosis of pneumoconiosis. we newly developed a procedure to recognize small rounded opacities in the lung and measure the density of those opacities, by employing a difference type filtering followed by a patten classification algorithm. By combining the set of the same type of filters with different mask sizes we derived a procedure to execute both the density classification and the size classification of pneumoconiosis in a unified way. We applied the procedure to the ILO international standard film set with the promising result. Furthermore we tried the recognition of rounded opacities from x-ray CT images of pneumoconiosis and showed its feasibility.(3) Two kinds of binary operations two pictures were applied computer diagnosis of chest x-ray images. One is detection of lung cancer from energy subtraction DR images. This revealed characteristics of energy subtraction images and the feasibility of computer interpretation of such images. The other is automated detection and measurement of the amount of changes among picture, sequences taken at different times from the same patient. We developed a procedure consisting of registration by the affine transformation and change detection by the local cross correlation. and applied it to the lung cancer diagnosis.