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Development of computer-aided diagnosis system for autopsy imaging

尸检成像计算机辅助诊断系统的研制

基本信息

批准号：
17360190
负责人：
KOBATAKE Hidefumi
金额：
$ 9.54万
依托单位：
Tokyo University of Agriculture and Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17360190/
关键词：
Autopsy Imaging Image Processing Computer-aided Diagnosis CT Bone Fracture Ai 死亡画像病理診断診断支援 CAD

项目摘要

First, we established an interface system for transferring the data from the CT scanner in Chiba University to personal computer for the image processing and built an image database for this study. Then we focused on head and thorax in CT images to develop an bone fracture detection system since bone fractures are one of the most important cause of death Followings are main achievements of this study.1. HeadThe proposed algorithm detected a symmetric plane of the head in 3D CT image and subtracted the gray values in the left part and their corresponding values in the right parts to enhance the difference. The variation of the subtracted values were model statistically using eigen-image method We proposed and evaluated the bone fracture detection method which is based on the results of the analysis.2. ThoraxWe developed the following procedure for detecting bone fracture in the thorax CT imagesa) extraction of bone areab) classification of bones based on human anatomyc) detection of bone fractureIn the process a), we proposed not only the binarization based method but also fine texture based method. We have applied the process to the CT images in the database and confirmed that it could extract the bone region successfully. The process b) has the statistical model of the point set which can classify the bones into anatomical elements. From the experimental results, it was found that the success rate of the anatomical labeling was about 70%. Finally the last process detected locations of bone fracture based on the continuity of the bone surface. The experimental results showed that it could detect about 70% of the bone fractures with some false positives.

首先，我们建立了一个接口系统，将数据从千叶大学的CT扫描仪传输到个人计算机进行图像处理，并建立了本研究的图像数据库。针对骨折是导致死亡的重要原因之一，本文重点研究了CT图像中的头、胸部位，开发了一套骨折检测系统.该算法在三维CT图像中检测出头部的一个对称平面，并将头部左半部分的灰度值与其右半部分的灰度值相减，以增强差值。利用特征图像法对差值的变化进行统计建模，并在此基础上提出了骨折检测方法，并对该方法进行了评价. Thorax我们开发了以下过程来检测胸部CT图像中的骨折a）骨区域的提取b）基于人体解剖学的骨分类c）骨的检测在过程a）中，我们不仅提出了基于二值化的方法，而且提出了基于精细纹理的方法。我们已经将该过程应用于数据库中的CT图像，并证实它可以成功地提取骨区域。过程B）具有点集的统计模型，该模型可以将骨骼分类为解剖元素。根据实验结果，发现解剖标记的成功率约为70%。最后一个过程根据骨表面的连续性检测出骨折的位置。实验结果表明，它可以检测到约70%的骨折，但存在一些假阳性。