This course has two components. On the one hand, it is an introduction to digital image analysis presenting selected fundamental problems in medical image analysis, computer vision, photo/video editing, and graphics. We cover such basic concepts as image segmentation, registration, object recognition/matching, tracking, texture, etc. On the other hand, this is an applied course on standard computer science algorithms where students develop practical understanding of dynamic programming, graph based algorithms, computational geometry methods, etc. In fact, image analysis provides a stimulating environment for studying algorithms as their outputs can be intuitively visualized. Students with previous background in algorithms will be exposed to applications in image analysis, while students already familiar with problems in imaging will learn efficient methods based on standard CS algorithms. The course emphasizes the design, analysis, and implementation of algorithms in the context of 2D/3D medical images, photo and video data.
- 2D/3D/4D Registration
- Biomedical Image Databases / Retrieval
- Biomedical Image Motion Analysis
- Biomedical Image Reconstruction Methods
These are lecture notes which I used to study my course related to image processing. I found these medical image processing lecture notes very useful while studying for the subjects even at the last minute. They helped me in overcoming the fear related to image processing . These notes are meant for all those people who are just beginners and know nothing related to medical image processing. They are well framed and collected
Do download the notes from links given below
Advanced Image Processing Lab Practicals
Practicals for graduate & undergraduate students……
Lab. 1. Image Digitization: Discretization
Lab. 2. Image Digitization: Quantization
Lab. 3. Image Coding: Predictive Methods
Lab. 4. Image Coding: Transform Methods
Lab. 5. Image Global and Local Statistics
Lab. 6. Statistical Image and Noise Models and Noise Diagnostics
Lab. 7. Image Resampling and Geometrical Transformations
Lab. 8. Target Location and Object Detection: Localization Accuracy and Reliability
Lab. 9. Target Location and Object Detection in Clutter Images
Lab. 10. Linear Filters for Image Restoration and Enhancement
Lab. 11. Rank Filters for Image Restoration, Enhancement and Segmentation
Lab. 12. Image Perfecting and Enhancement
Digital image processing requires a lot of practice but to do proper practice @ home we require a lab manual which we can refer from time to time and it must contain all the necessary and relevant information about the lab
Today while I was searching for lab manual for a lab I got this manual and would like to share it with you
This lab manual contains following topics
Lecture Notes-CLICK ON THE TOPICS TO DOWNLOAD THEM DIRECTLY
- Topic 1. Introduction
- Topic 2. Overview of different image modalities: photo images, video, and 2D-3D-4D medical data
- Topic 3. Overview of basic image processing (point and local neighborhood processing): gamma correction, histogram equalization, window-center adjustment, linear filtering, image gradients.
- Topic 4. Basic image segmentation in 2D (thresholding, region-growing, mean-shift, live-wire).
- Topic 5. Deformable models (snakes): gradient descend, DP-snakes. Also distance transforms and generalized distance transforms.
- Topic 6. Beyond snakes: implicit vs. explicit representation of boundaries, level-sets, geodesic active contours, geometric energy functionals.