Learning Tomography: Non-centered projections

Why is my reconstruction so bad ?
This question often arises when the resulting image (or volume) is so disappointing... Specially when you are working with experimental projections, new artifacts and defects are appearing never met before in this series Learning Tomography where all the images are always perfect.
Thus, it is time to show some of these defects... to understand how to fix them.

Gray-level images and morphology

In MM [TOC], the gray-level images are considered as a stack of binary images, what they called level sets. Here is a small demonstration using ImageJ and an application to dilation.

Learning Tomography: Blur effect

In the simple Back-projection technique, the image appears blurry and we need to pre-filter the sinogram with a ramp filter before computing the 2D reconstruction. Why is this blur effect not observed in the Direct Fourier Reconstruction technique?

3D sample: The Borrromean Rings

In the collection of samples I used for tomography, there is the so-called "Borromean Rings" composed of three interleaved tori [Link].

Learning Tomograhy: New version of back projection

In this series Learning Tomography, I mentioned a backProjImproved.js script in several posts to compute a 2D reconstruction according to the backprojection technique.... Unfortunately, I have never published it. Here it  is...

Analyze

Table of Contents

Analyze objects of interest is the last step of a usual pipeline of image processing and allows to quantify or describe image features...

Crazybiocomputing; The mini-games

This blog CrazyBiocomputing − dedicated to image processing using ImageJ − is completed by a series of mini-games located in another CrazyBiocomputing web page [Link]. This is a good opportunity to practice with ImageJ and work/understand the various concepts described in the posts of this blog. Here are some explanations about how it works...