Development of the next generation CAD system for mammography

开发下一代乳腺X线摄影CAD系统

• 批准号: 13555115
• 负责人: KOBATAKE Hidefumi
• 金额: $ 8.7万
• 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: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-13555115/
• 关键词: computer aided diagnosis; screening of breast cancer; mammography; tumor detection; cluster of microcalcifications; feature selection; 特徴量選択; CAD; 乳がん; 検診; スクリーニング; 乳房X線像

The performance of the computer aided diagnosis system for mammography has been greatly improved from the view points of the area under the ROC curve. The primary results are as follows.1) Coarse calcifications can cause false microcalcification detections. Theirs detection system has been developed, which is used to eliminate false microcalcification detections caused by coarse calcifications. The performance of the revised microcalcification detection system has been greatly improved.2) A method for discriminating between true and false microcalcifications has been developed. It uses features obtained from the Wavelet transform of original mammograms. The false positive detection rate of the new system is about one third as compared with the conventional system.3) A large scale experiments to investigate a semi-optimal feature set for discriminating between true and false tumors have been performed. More than 490 features have been examined from the view point of the area under the ROC curve. Among them, 50 features have been selected as a semi-optimal feature set. The performance of the tumor detection system adopting the semi-optimal feature set is excellent. It has been proven to be effective in decreasing false positive detections. The false positive detection rate of the new system is as low as a half of the conventional system

从ROC曲线下面积的角度来看，乳腺X线摄影计算机辅助诊断系统的性能有了很大的提高。主要结果如下：1）粗钙化点会导致错误的微钙化检测。他们的检测系统已经开发，这是用来消除错误的微钙化检测所造成的粗钙化。改进后的微钙化点检测系统的性能得到了很大的提高。2）提出了一种鉴别真假微钙化点的方法。它使用从原始乳房X线照片的小波变换获得的特征。新系统的假阳性检测率约为传统系统的三分之一。3）进行了大规模的实验，以研究用于区分真假肿瘤的半最佳特征集。从ROC曲线下面积的角度研究了490多个特征。其中，50个特征已被选为半最佳特征集。采用半最优特征集的肿瘤检测系统性能良好。它已被证明是有效的，在减少假阳性检测。新系统的误检率仅为传统系统的一半

项目成果

B.Tsagaan, A.Shimizu, H.Kobatake, K.Miyakawa: "An Automated Segmentation Method of Kidney Using Statistical Information"Proc.MICCAI. Part I. 556-563 (2002)

B.Tsagaan、A.Shimizu、H.Kobatake、K.Miyakawa：“使用统计信息的肾脏自动分割方法”Proc.MICCAI。

小畑秀文: "3次元画像とモロフォロジー"日本医用画像工学会誌. 9・3. 168-173 (2001)

Hidefumi Obata：“三维图像和形态学”日本医学影像工程学会杂志 9・3（2001）。

古屋早知子, 魏軍, 萩原義裕, 清水昭伸, 小畑秀文, 縄野繁: "特徴量選択による乳房X線像上の悪性腫瘤影判別能力の改善と選択基準の評価"電子情報通信学会論文誌. Vol.J86-D・II, No.5. 587-597 (2003)

Sachiko Furuya、Wei Jun、Yoshihiro Hagiwara、Akinobu Shimizu、Hidefumi Obata、Shigeru Nawano：“通过特征选择和评估选择标准，提高区分乳房 X 光照片上恶性肿瘤阴影的能力”，电子信息通信工程师学会杂志， J86-D·II 卷，第 587-597 号（2003 年）

J. Wei, Y. Hagihara, H. Kobatake: "Detection of Lung Nodules on Digital Chest Radiographs"Medical Imaging Technology. 19,No. 6. 468-476 (2001)

J. Wei、Y. Hagihara、H. Kobatake：“数字胸部 X 光片上肺结节的检测”医学成像技术。

N. Mizusawa, Y. Hagihara, A. Shimizu, H. Kobatake, H. Takeo and S. Nawano: "Coarse Calcification Detection Method and its Application to Reducing False Microcalcification Clusters"Medical Imaging Technology. 20, No. 3. 203-211 (2002)

N. Mizusawa、Y. Hagihara、A. Shimizu、H. Kobatake、H. Takeo 和 S. Nawano：“粗钙化检测方法及其在减少假性微钙化簇中的应用”医学成像技术。