Research on Automatic Screening System for Lung Cancer Detection.

肺癌检测自动筛查系统的研究。

• 批准号: 14580419
• 负责人: YAMAMOTO Shinji
• 金额: $ 2.24万
• 依托单位: Toyohashi University of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2002
• 资助国家: 日本
• 起止时间: 2002 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14580419/
• 关键词: Lung Cancer; Image Processing; Pattern Recognition; Computer Aided Diagnosis; CT Image

We proposed to develop an automatic screening system for lung cancer detection utilizing CT scanner images. The most important subject to perform this purpose is that the detection accuracy of lung nodules by computer should be realized more than 10 times higher level of present technical status.In this research activity, we developed the following three different algorithms for feature ectraction and recognition, and realized efficient detection accuracy by combining these three algorithms.(1)Development of a classification algorithm between abnormal nodules and normal vessels based on 3-D artificial structure models. Abnormal nodule models are produced as 3D sphere with various kinds of radii. Normal vessel models are produced as the combination of two or three cylinders with various kinds of connecting angles and radii. Input image is compared with all of these models and best fitting model is selected which correspond to final decision (abnormal or normal).(2)Development of a classification algorithm based on principal component analysis method (PCA) of CT intensity information in the region of interest(ROI).(3)Development of a classification algorithm based on heuristic feature extraction and statistical discriminant function.

我们建议开发一个利用CT扫描仪图像进行肺癌检测的自动筛查系统。实现这一目标的最重要的课题是使计算机对肺结节的检测准确率达到目前技术水平的10倍以上，在本研究活动中，我们开发了以下三种不同的特征提取和识别算法，并将这三种算法相结合，实现了有效的检测准确率。（1）基于三维人工结构模型的异常结节和正常血管之间的分类算法的开发。异常结节模型被生成为具有各种半径的3D球体。常规的血管模型是由两个或三个具有不同连接角度和半径的圆柱体组合而成。将输入图像与所有这些模型进行比较，并选择与最终决策（异常或正常）相对应的最佳拟合模型。（2）提出了一种基于感兴趣区（ROI）CT强度信息主成分分析（PCA）的分类算法。（3）提出了一种基于启发式特征提取和统计判别函数的分类算法。

项目成果

Hotaka Takizawa, etc.: "A RECOGNITION METHOD OF LUNG NODULE SHADOWS IN X-RAY CT IMAGES USING 3D OBJECT"International Journal of IMAGE AND GRAPHICS. Vol.3,No.4. 533-545 (2003)

Hotaka Takizawa等：“使用3D OBJECT的X射线CT图像中肺结节阴影的识别方法”国际图像与图形杂志。

Mitsuhiro Tanino, etc.: "A Detection Method of Ground Glass Opacities in Chest X-ray CT Images using Automatic Clustering Techniques"Conference on SPIE Medical Imaging 2003. Vol.5032. 1728-1737 (2003)

Mitsuhiro Tanino等：“使用自动聚类技术的胸部X射线CT图像毛玻璃混浊的检测方法”SPIE医学影像会议2003年。Vol.5032。

滝沢穂高, 他: "3次元マルコフ確率場モデルを用いた胸部X線CT像からの結節陰影の認識"電子情報通信学会論文誌. J85-D-II, 9. 1401-1412 (2002)

Hotaka Takizawa 等人：“使用三维马尔可夫随机场模型识别胸部 X 射线 CT 图像中的结节阴影”，电子、信息和通信工程师学会汇刊 J85-D-II，9. 1401。 -1412 (2002)

滝沢穂高ほか: "3次元マルコフ確率場モデルを用いた胸部X線CT像からの結節陰影の認識"電子情報通信学会論文誌. J85-D-II(9). 1401-1412 (2002)

Hotaka Takizawa 等人：“使用三维马尔可夫随机场模型识别胸部 X 射线 CT 图像中的结节阴影”电子、信息和通信工程师学会汇刊 J85-D-II(9)。 -1412 (2002)

Gentaro Fukano, etc.: "Lung Nodules Recognition in Chest X-ray CT Images Using Subspace Method"Computer Assisted Radiology and Surgery 2003. CARS2003. 1390 (2003)

Gentaro Fukano等：“使用子空间方法进行胸部X射线CT图像中的肺结节识别”计算机辅助放射学和外科2003年。CARS2003。