Computer aided diagnosis system for lung cancer CT images

本文报告了计算机辅助CT诊断肺癌（LSCT）的图像处理技术。LSCT是我们项目组最新开发的用于肺癌普查的移动式CT扫描仪。在这种新的LSCT系统中，一个重要的问题是将图像信息从1张X光片增加到每人30张左右。我们试图通过图像处理技术来大幅度地减少要显示给医生的图像信息，如下所述。NO.1方法（MIP方法）MIP方法（最大强度投影）被考虑，其中由患者肺组织的30个切片组成的三维信息通过在二维平面上的投影来显示。最大密度投影法应用于LSCT的关键问题是预先去除可能掩盖病理阴影的干扰器官信号。我们开发了一种新的和稳定的方法，它使用数学形态学，分裂-Quoit滤波器，模型信息，和活动轮廓模型（蛇）。这种方法适用于68例患者样本，结果是有前途的。NO.2方法（Quoit滤波器方法）介绍了一种新的方法，自动识别候选区域的病理阴影。通过只向医生显示包含候选区域的切片，可以大大减少诊断的横截面数量，存在于肺野中的肺癌阴影看起来孤立且面积小。利用这一特性，二维、三维条件滤波器本文提出了一种基于数学形态学的Q滤波器和MIP-2D-Q滤波器自动提取孤立阴影的方法，并对68例病人1809幅图像进行了应用，使医生诊断的显示图像减少到144幅，减少率为8%（2.1张/例）。其中，5例癌症患者被正确提取。

This paper reports the image processing technique for computer-aided diagnosis of lung cancer by CT (LSCT). LSCT is the newly developed mobile-type CT scanner for the mass screening of lung cancer by our project team. In this new LSCT system, one essential problem is the increase of image informantion to about 30 slices per person from 1 X-ray film. We tried to reduce the image information drastically to be displayd for the doctor, by image processing techniques, as follows.NO.1 approach (MIP method)MIP mehtod (maximum intensity projection)is considered, where the three-dimensional information composed of 30 slices of patient's lung tissue is displayd by a projection on the two-dimensional plane. The most important problem to apply MIP method for LSCT is to delete beforehand the interfering organ signals that could mask the pathological shadows. We developed a new and stable method for it using mathematical morphology, Split-Quoit filter, model information, and active contour model (snake).This method is applied for 68 patient samples and the result is promissing.NO.2 approach (Quoit filter method)A new method is introduced which automatically recognizes the candidate regions for the pathological shadows. By displaying only the slices containing the candidates regions to the doctor, the number of cross sections to bediagnosed can be drastically reduced.The shadow of the lung cancer which exists in the lung field, appears isolated and with a small area. By utilizing this property, 2-D , 3-D Quoit filters (Q-filters) and MIP-2D-Q filter are developed to extract the isolated shadow automatically, where Q-filters are those based on mathematical morphology.This method is applied for 68 patient samples including 1809 images, and the number of displayd images to be diagnosed by the doctor is reduced to 144 images, which means the reductin rate 8% (2.1 images/patient). Among those, the 5 cancer patients are extracted correctly.