Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 345892 Digital Image Processing 1 3.0 3 ★ Shih-Chun Jin 4EOE4 1 0 光電四、光電所和電資外生班合開,逾修課人數上限加選

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	345892	Digital Image Processing	1	3.0	3	★	Shih-Chun Jin	4EOE4	1	0	光電四、光電所和電資外生班合開,逾修課人數上限加選

Course Plan：

Instructor Shih-Chun Jin

Email a6210647@nycu.edu.tw

Last Updated 2024-12-25 15:06:46

Course Objective The course aims to learn how digital images are practiced in real-world applications, especially in medical images and computer vision. This class will introduce the image's composition, image adjustment, composition, and even feature extraction for radiomics to apply to image detection and recognition. We will introduce some popular image processing algorithms about image processing and implement them in medical imaging projects.

Course Schedule Week Topic Week 1 What is Digital Image Processing-Digital Image Fundamentals Week 2 Introduction to Python environment setting Week 3 Introduction to Medical Image Format Week 4 Intensity Transformations, Spatial Filtering, and kernel function (I) Week 5 Intensity Transformations, Spatial Filtering and kernel function (II) Week 6 Project Report (1) Week 7 Intensity-Based Segmentation Week 8 Morphological Image Processing Week 9 Feature Selection and Classifying Week 10 Project Report (2) Week 11 Fourier Properties and Filtering in the Frequency Domain (1) Week 12 Fourier Properties and Filtering in the Frequency Domain (2) Week 13 Fourier Properties and Filtering in the Frequency Domain (3) Week 14 Project Report (3) Week 15 Image restoration Week 16 Image reconstruction Week 17 Feature extraction, representation, and radiomics for medical Week 18 Project Report (4)

Evaluation and grading policy (1) Homework 40% (2) Project report 40% (3) Participation 20%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Digital Image Process 4/e Gonzalez & Woods 2. The Image Processing Handbook 5/e John C. Russ 3. Medical Image Reconstruction: A Conceptual Tutorial Gengsheng Lawrence Zeng

The access to curricular consultation E-mail to a6210647@ntut.edu.tw

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI

Note
Python programming skills are needed.
Please prepare your laptop for over limitation
「逾修課人數上限加選者，上課請自備筆電」

Instructor	Shih-Chun Jin
Email	a6210647@nycu.edu.tw
Last Updated	2024-12-25 15:06:46
Course Objective	The course aims to learn how digital images are practiced in real-world applications, especially in medical images and computer vision. This class will introduce the image's composition, image adjustment, composition, and even feature extraction for radiomics to apply to image detection and recognition. We will introduce some popular image processing algorithms about image processing and implement them in medical imaging projects.
Course Schedule	Week Topic Week 1 What is Digital Image Processing-Digital Image Fundamentals Week 2 Introduction to Python environment setting Week 3 Introduction to Medical Image Format Week 4 Intensity Transformations, Spatial Filtering, and kernel function (I) Week 5 Intensity Transformations, Spatial Filtering and kernel function (II) Week 6 Project Report (1) Week 7 Intensity-Based Segmentation Week 8 Morphological Image Processing Week 9 Feature Selection and Classifying Week 10 Project Report (2) Week 11 Fourier Properties and Filtering in the Frequency Domain (1) Week 12 Fourier Properties and Filtering in the Frequency Domain (2) Week 13 Fourier Properties and Filtering in the Frequency Domain (3) Week 14 Project Report (3) Week 15 Image restoration Week 16 Image reconstruction Week 17 Feature extraction, representation, and radiomics for medical Week 18 Project Report (4)
Evaluation and grading policy	(1) Homework 40% (2) Project report 40% (3) Participation 20%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Digital Image Process 4/e Gonzalez & Woods 2. The Image Processing Handbook 5/e John C. Russ 3. Medical Image Reconstruction: A Conceptual Tutorial Gengsheng Lawrence Zeng
The access to curricular consultation	E-mail to a6210647@ntut.edu.tw
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI
Note	Python programming skills are needed. Please prepare your laptop for over limitation 「逾修課人數上限加選者，上課請自備筆電」