Chapter 9 : Biomedical Imaging Informatics

After reading this chapter, you should know:

What makes images a challenging type of data to be processed by computers when compared to non-image clinical data?
Why are there many different imaging modalities, and by what major two characteristics do they differ?
How are visual and knowledge content in images represented computationally? How are these techniques similar to representation of non-image biomedical data?
What sort of applications can be developed to make use of the semantic image content made accessible using the Annotation and Image Markup model?
What are four different types of image processing methods? Why are such methods assembled into a pipeline when creating imaging applications?
What is an imaging modality with high spatial resolution? What is a modality that provides functional information? Why are most imaging modalities not capable of providing both?
What is the goal in performing segmentation in image analysis? Why is there more than one segmentation method?
What are two types of quantitative information in images? What are two types of semantic information in images? How might this information be used in medical applications?
What is the difference between images registration and image fusion? What are examples of each?

The 80/20 Rule

9.4.1 Types of Image-Processing Methods
9.4.2 Global Processing
9.4.3 Image Enhancement
9.4.4 Image Rendering / Visualization
9.4.5 Image Quantitation
- 9.4.5.1 Quantitative Image Features
- 9.4.5.2 Image Patches
9.4.6 Image Segmentation
- 9.4.6.1 Region-Based and Edge-Based Segmentation
- 9.4.6.2 Model- and Knowledge-Based Segmentation
- 9.4.6.3 Combined Methods
- 9.4.6.4 Parametric and Non-Parametric Clustering for Segmentation
9.4.7 Image Registration
- 9.4.7.1 Voxel Registration
- 9.4.7.2 Volume Registration
  9.5 Image Interpretation and Computer Reasoning