Image Analysis

Period: 2nd (Spring semester)


Course contents:

Topic 1: Elementary concepts

1. Bit, byte, pixel, voxel, formats of image, dynamic range, histogram, LUTs, mathematical operations with images.

2. Sampling: theorem of Shannon-Whittaker-Nyquist.

3. Fourier’s space, operations in the space of frequency with images.

4. Segmentation, types of noise and filters in the real and Fourier spaces.

5. Projection and back-projection.

6. Visualization 3D, ImageJ’s utilization, use of plugins, Macro programming.

Topic 2: Quantification of the image

1. Densitometry.

2. Granulometry

Topic 3: Signal processing and analysis

1. Periodic signal, 2D crystals, stacks, helix: periodic signals, lattice vectors, stacks and helix parameters.

2. Signal to noise ratio.

3. Denoising

Topics 4: 3D Reconstruction from projections

1. Reconstruction algorithms used in diagnosis by images and biology: theorem of the central section, back-projection in Fourier’s space, weighted back-projection, algebraic reconstruction technics.

2. Effects of sub-sampling and loss of information.

Topic 5: Return and segmentation of volumes

1. Manual segmentation.

2. Semi-automatic segmentation



 Recommended textbooks:

⋅Electron tomography. J Frank. Ed Springer. 2nd edition. ISBN: 0-387-31234-X

⋅Digital Image processing. An algorithmic introduction using Java. W. Burger & M.J. Burge. Ed. Springer. ISBN: 978-1-84628-379-6


Coordinador: Sergio Marco Garrido




More info on the course official guide (Guía docente)