| 課程名稱 |
(中) 醫學影像處理(6658) |
| (Eng.) Medical Image Processing |
| 開課單位 |
資工系 |
| 課程類別 |
選修 |
學分 |
3 |
授課教師 |
李奇恩 |
| 選課單位 |
資工系 / 碩士班 |
授課使用語言 |
中文 |
開課學期 |
1142 |
| 課程簡述 |
This course introduces the fundamental principles and practical techniques of medical image processing used in modern healthcare and biomedical research. Students will learn how medical images are formed, represented, enhanced, analyzed, and interpreted, with a focus on common imaging modalities such as X-ray, CT, MRI, ultrasound, and nuclear medicine. Core topics include image preprocessing, filtering, segmentation, feature extraction, registration, and quantitative analysis. The course also covers basic concepts of machine learning and deep learning as applied to medical images, along with clinical considerations and evaluation methods. Through lectures and hands-on exercises, students will develop the ability to design and implement image processing methods for real-world medical applications. |
| 先修課程名稱 |
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| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
| The objective of this course is to equip students with a solid understanding of the theoretical foundations and practical techniques of medical image processing. By the end of the course, students are expected to develop the ability to interpret and process medical images from different imaging modalities, apply appropriate algorithms for image enhancement, segmentation, and analysis, and understand how these techniques support clinical decision-making. The course also aims to cultivate practical skills in implementing image processing methods and to foster an awareness of current trends and challenges in medical imaging research and applications. |
| 1.具備資訊科學素養、資訊理論與數學分析之能力 |
| 3.具備分析、設計與實作資訊軟體系統之能力 |
| 4.具備分析、設計與整合資訊應用系統之能力 |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習) |
| 週次 |
授課內容 |
| 第1週 |
Introduction for the scope of medical image processing, its role in healthcare, and an overview of medical imaging modalities and clinical applications. |
| 第2週 |
The fundamentals of image formation and image representation, including sampling, quantization, and basic image characteristics. |
| 第3週 |
Image enhancement techniques in the spatial and frequency domains, with emphasis on noise reduction and contrast improvement. |
| 第4週 |
Image filtering methods, including linear and non-linear filters, and their applications in medical images. |
| 第5週 |
Edge detection and feature extraction techniques commonly used for anatomical structure analysis. |
| 第6週 |
Image segmentation methods, including thresholding, region-based approaches, and basic model-based techniques. |
| 第7週 |
Morphological image processing and its applications in medical image analysis. |
| 第8週 |
Midterm exam |
| 第9週 |
Image registration and alignment, including rigid and non-rigid registration techniques. |
| 第10週 |
3D medical image processing and visualization, including volume rendering and multi-planar reconstruction. |
| 第11週 |
Quantitative image analysis and radiomic feature extraction for clinical and research applications. |
| 第12週 |
Machine learning methods for medical image analysis, including classical classifiers and evaluation metrics. |
| 第13週 |
Deep learning approaches for medical imaging, such as convolutional neural networks and their applications. |
| 第14週 |
Clinical validation, performance evaluation, and common challenges in medical image processing. |
| 第15週 |
Current research trends and emerging topics in medical imaging, including multimodal imaging and AI-assisted diagnosis. |
| 第16週 |
Project presentations |
自主學習
內容 |
02.閱覽產業及學術相關多媒體資料
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| 學習評量方式 |
Attendance: 10%
Homework: 25%
Midterm exam: 30%
Final Project: 35% |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
Digital Image Processing (3rd Edition), R. C. Gonzalez, R. E. Woods, Prentice-Hall, 2008.
Image Processing, Analysis, and Machine Vision (4th Edition), M. Sonka, V. Hlavac, R. Boyle, CL Engineering, 2014.
Deep Learning for Medical Image Analysis, S. Kevin Zhou, Hayit Greenspan and Dinggang Shen, Elsevier, 2017. |
| 課程教材（教師個人網址請列在本校內之網址） |
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| 課程輔導時間 |
| Tuesday 14:00-16:00 |
| 聯合國全球永續發展目標(連結網址) |
| 04.教育品質 08.就業與經濟成長 | 提供體驗課程：Y |
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