Grant-in-Aid for Scientific Research (B)
|Allocation Type||Single-year Grants |
|Research Institution||Nagoya University |
MORI Kensakuk Dept. of Media Science, Graduate School of Information Science, Assistant Professor, 情報科学研究科, 助教授 (10293664)
SUENAGA Yasuhito Dept. of Media Science, Graduate School of Information Science, Professor, 情報科学研究科, 教授 (60293643)
KITASAKA Takayuki Dept. of Media Science, Graduate School of Information Science, Research Associate, 情報科学研究科, 助手 (00362294)
HIRANO Yasushi Nagoya University, Information Technology Center, Assistant professor, 情報連携基盤センター, 助教授 (90324459)
TORIWAKI Jun-ichiro Chukyo University, School of Life System Science and Technology, Professor, 生命システム工学部, 教授 (30023138)
MEKADA Yoshito Chukyo University, School of Life System Science and Technology, Professor, 生命システム工学部, 教授 (00282377)
|Project Period (FY)
2003 – 2005
Completed (Fiscal Year 2005)
|Budget Amount *help
¥16,400,000 (Direct Cost: ¥16,400,000)
Fiscal Year 2005: ¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2004: ¥6,300,000 (Direct Cost: ¥6,300,000)
Fiscal Year 2003: ¥6,800,000 (Direct Cost: ¥6,800,000)
|Keywords||navigation diagnosis / segmentation / anatomical knowledge database / computer aided diagnosis / real-time navigation / user interface / CT image / virtual human body / CT画像 / 仮想化内視鏡 / 可視化 / コンピュータ支援画像診断 / 臓器アトラス|
Purpose of this research was to develop an intelligent navigation based diagnosis system of medical images based on navigation of virtual human body. We discussed a completely novel system which enables a medical doctor to diagnose medical images efficiently by automatically detecting the locations and status of abnormal regions and by superimposing them on internal views of virtualized human body.
(1) Development of real-time navigation method based on high dimensional medical images
Input image of this system is three or four dimensional image which includes precise human body information. We investigated fast navigation methods inside virtualized human body
(2) Construction of anatomical knowledge database
Storage methods of anatomical knowledge of chest and abdominal organs were discussed. Here, we investigated representing methods of anatomical knowledge on computer. As examples of anatomical knowledge database, bronchus name database and organ atlases were constructed.
of recognition methods of organs
Using anatomical knowledge database constructed above, we developed a method for recognizing multi organs simultaneously.
(4) Development of methods for recognizing and understanding high dimensional medical images
Using the anatomical knowledge database, algorithms for detecting, classifying and comparing abnormal regions were examined. As an example of integration of multi modal images, image processing methods for PET-CT images were investigated.
(5) Development of real-time displaying algorithm of assistance information of diagnosis
We discussed algorithms for displaying assistance information such as results of abnormal region detection and anatomical structure recognition in real time. Here, assistance information includes not only marking results of abnormal regions but also anatomical names concerning with the regions.
(6) Practical evaluation of the system in the clinical field
We created a prototype system of navigation diagnosis by integrating the methods we have developed. The system was evaluated by clinicians. Less