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Computer aided diagnosis system for lung cancer CT images

肺癌CT图像计算机辅助诊断系统

基本信息

批准号：
05558033
负责人：
YAMAMOTO Shinji
金额：
$ 7.1万
依托单位：
Toyohashi University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research (B)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-05558033/
关键词：
Lung cancer Image prosessing Pattern recognition Computer aided diagnosis CT image

项目摘要

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.

本文报道了用于计算机辅助CT诊断肺癌的图像处理技术。LSCT是我们项目组最新开发的移动式肺癌筛查CT扫描仪。在这种新的LSCT系统中，一个根本的问题是图像信息量从1张X光片增加到每人约30张。我们试图通过图像处理技术来大大减少要显示给医生的图像信息，具体如下：1号方法(MIP方法)MIP方法(最大强度投影)，通过在二维平面上的投影来显示由30个肺组织切片组成的三维信息。将MIP方法应用于LSCT，最重要的问题是事前剔除可能掩盖病变阴影的干扰器官信号。将数学形态学、分裂Quoit滤波、模型信息和活动轮廓模型(Snake)相结合，提出了一种新的稳定的阴影识别方法。该方法应用于68例患者，取得了令人满意的结果。通过只将包含候选区域的切片显示给医生，可以大大减少要诊断的横断面的数量。利用这一特性，开发了基于数学形态学的二维、三维Quoit滤波器(Q-Filters)和MIP-2D-Q滤波器来自动提取孤立阴影，该方法应用于68例患者样本，包括1809张图像，医生诊断的显示图像数量减少到144张，归约率为8%(2.1张/患者)。其中，5名癌症患者被正确提取。