Basic Information

  • Course Code: CS 736
  • Course Name: Algorithms for Medical Image Processing
  • Course Offered In: 2023- 2024
  • Semester Season: Autumn
  • Instructors: Prof. Suyash Awate
  • Prerequisites: Any statistics or probability course
  • Difficulty (1 being easy and 5 being tough): 4

Course Content

The topics covered in this course are as follows –

Introduction to Mathematical imaging models Noise models and Image Denoising X-Rays and Computed Tomography (CT) Magnetic resonance imaging (MRI) Image segmentation Image denoising Methods Kernel PCA Anatomical shape analysis Methods Image registration Methods

Feedback on Lectures

The instructor taught with the help of slides, occasionally using the board to derive or explain an algorithm. Paying attention in class is highly advised since it makes preparation for exams easier. The instructor readily solved the doubts and was jovial while teaching. Classes were held in morning slots, 2 days a week for 1.5 hours each.

Feedback on Evaluations

There were a total of 4 assignments given based on the implementation of the material taught in class. Assignments demanded considerable amount of time but they also helped in understanding of the material taught in class which made exams seem easy.

There was an end semester exam, a mid semester exam and 2 quizzes. The exams were closed book (cheat sheets were not allowed), based on the material covered in the class. They were moderate in difficulty level, and mostly based on the material discussed in class. Easy to score if basic concepts of each topic is understood and material is thoroughly revised, not much rote learning required.

The weightages were:

Assignments – 0%

Quizzes-35%

Midsem and Endsem – 50% (individual weightages not known)

Course Project – 15%

All Weightages were subject to +-10% change

Study Material and Resources

The content uploaded on moodle was more than enough.

Follow-up Courses

None

Final Takeaway

The course deals with basic algorithms used in medical image processing and thus it is a really good course for those interested in hard core mathematics, optimization algorithms in action and ML.