Medical imaging is an exploding field.
The technologies for visualizing the body (the imaging modalities) are becoming very powerful, providing exquisite images of tissue morphology, and revealing tissue function.
Imaging is at the core of medical practice; nearly all patients have imaging of some sort during care and many studies produce thousands of images.
The growth in digital imaging is necessitating techniques for medical image processing and analysis.
This course will provide an overview of fundamental image processing concepts and algorithms applied to medical imaging data.
The course will cover the following topics:
Introduction to the field of medical image processing and its applications;
2D signal processing;
2D discrete Fourier transform and its application in medical imaging (MRI and CT reconstruction);
Image enhancement (histograms, denoising, sharpening);
Image quantization; Image Restoration; Compression; DICOM format; Deep-learning methods for medical images;
Python programming language for medical image processing.
Students acquire the ability to:
- implement medical image processing algorithms in Python programming language.
- determine which algorithm is suitable to solve a specific challenge in medical image processing.
- develop algorithms to solve specific challenges in medical image processing.
Course Title: Introduction to Medical Image Processing
Course Ref. Num. 336027
Num. or Credits: 2.5
Course Times: Lecture - 14:30-16:30: WEDNESDAY;
Recitation - 9:30-10:30: THURSDAY;
Course Locations: Lecture: Silver Building, Room 202; Recitation: Silver Building, Room 301
Prerequisites:
- 044131 אותות ומערכות
- 104016 אלגברה 1מ
- 094423 סטטיסטיקה
- 104034 הסתברות
Course Textbooks:
- Gonzalez and Woods, Digital Image Processing 4th edition, Pearson, 2017.
- Birkfellner, Applied Medical Image Processing: A Basic Course, CRC press, 2010.
Students will be introduced to:
- the main concepts of medical imaging.
- the main concepts of medical image processing algorithms:
- - 2D Fourier transform
- - Medical image reconstruction
- - Image enhancement
- - Image quantization
- - Image compression
- - Image restoration
- - Deep-leanring methods for medical image processing
- the DICOM format for medical imaging data storage.
- developing image processing algorithms for medical imaged MRI using the python programming language via hands-on learning.
