應用電腦影像處理(結合紅外線熱影像與可視光影像)，來輔助醫療診斷。 本專題在Windows上，使用Visual Studio的VC++作為開發平台，引用openCV的函式庫，對影像來源進行各種matrix運算、色階調整、影像疊加，產生目標的融合影像。並利用Python中Deep Learning的Keras套件，配合Oxford Visual Geometry Group的影像辨認模型，比較原始的熱影像與光影像與處理過的融合影像，發現該技術對提昇影像辨認率，具有相當大的潛力。 希望導入電腦數位影像處理，可以提昇醫療影像診斷的精確性與效率性。

1. A Project Report
Medical Applications of Infrared Thermal Image
Prepared by Elias Soong

2. Agenda
Introduction
Main-Courses
1) Image Fusion
2) Image Vectored
Conclusion

3. Introduction
Inflammation
1) Red
2) Swollen
3) Heat
4) Pain
Symptom of Heat
1) Infrared Thermal Device

4. Main-Courses::
Thermal Image Visible Image
IMAGE FUSION

5. Main-Courses::Image Fusion
Image Process
Pre-
Process
Zoom
Rotate
Shift
Source
Image
Thermal
Image
Visible
Image
Process-
ing
Alpha
Beta
Blend
Vectored
Target
Image
Fusion
Image
Vector
Image

6. Main-Courses::Image Fusion

7. Main-Courses::Image Fusion
Image Process
1) Shift
2) Zoom
3) Rotation
4) Blend
5) Vector

8. Main-Courses::Image Fusion
Before Shift
1) Horizontally = 0
2) Vertically = 0
After Shift
1) Horizontally = 20
2) Vertically = 20

9. Main-Courses::Image Fusion
Before Zoom
1) Ratio = 100%
After Zoom
1) Ratio= 80%

10. Main-Courses::Image Fusion
Before Rotation
1) Angle=90
After Rotation
1) Angle=85

11. Main-Courses::Image Fusion
Blend-1
1) Alpha=20
2) Beta=80
Blend-3
1) Alpha=80
2) Beta=20
Blend-2
1) Alpha=50
2) Beta=50

12. Main-Courses::Image Fusion
Save as bmp image Save as a svg image

13. Multi-Spectral Face Recognition - Fusion of Visual Imagery with Physiological Information
Main-Courses::Image Fusion
Image Fusion
1) Visible Image
2) Thermal Image
Diagnostics in Medical
1) Preciously
2) Efficiently
Applications
1) Image Recognition

14. Conclusion
Cons
1) Deviations of Patient and
Environment
2) Lack of Precious and Direct
Diagnostics
Pros
1) AI Development
Computer Vision
Deep Learning
2) IR Features
No Radiation
No Contact
No Invasive

15. Questions & Answers