I just finished reading Quantitative Biological Image Analysis by Erik Meijering and Gert van Cappellen (Chapter 2 of Imaging Cellular and Molecular Biological Functions, S. L. Shorte and F. Frischknecht (eds.), Springer-Verlag, Berlin, 2007, pp 45-70). It’s an excellent article! I especially liked the part that classify fields related to computer vision. I outline it below along with a few thoughts of my own. There are nice illustrations too but I won’t copy them.

Image Formation: object in -> image out. It may be important to know how the image was formed originally. The reason is that if you have a priori knowledge of the hardware that produced the image you may be able to use it to mitigate noise and imperfections (image processing below). However, it seems to me that the hardware manufacturers should be taking care of this.

Image Processing: image in -> image out. Smoothing, sharpening, blurring and de-blurring, other image enhancement, and on and on, hundreds of tasks with many different algorithms for each. Just take a look at Photoshop! Most of the ImageJ is also about image processing. Since the output is an image, the main purpose is to supply people with better looking images. A secondary purpose is preprocessing for image analysis to improve quality. The term “quality” is relative and depends on the context. One thing clear is that image processing leads to loss of information. That could harm your image analysis. So, you need insight into the problem to be sure that what you lose isn’t important. That insight comes from image analysis – full circle…

Image Analysis: image in -> features out. Let’s start with “dual purpose” image processing tasks. These operations are also image analysis tools:

• Intensity transformation – the value of each pixel is replaced with another value that depends only on the initial value. The main application is binarization (via thresholding or otherwise) as many image analysis tasks are applicable only to binary images.
• Local filtering - the value of each pixel is replaced with another value that depends only on the values of pixels in a certain neighborhood of this pixel. One of the main applications is edge detection (pixels where the values are changing the fastest). Morphology is another method of detecting edges (dilated version minus eroded version) but is limited to binary images. Unfortunately, neither method guarantees an unbroken sequence of edges. As a result, it may be impossible to reconstruct the object this sequence is supposed to surround.

There is more here than I expected. In the next installment:

• More of Image Analysis,
• Computer Graphics,
• Computer Vision, and
• Visualization.