Document Database

Do you have trouble keeping track of and organizing all the research papers
you download and read? Spend more time editing and arranging the
bibliography than actually writing that paper? BibTex entries drive you as
nuts as me ?

Well - there is an good piece of software called the Document Database

http://docdb.sourceforge.net/index.html - that is designed explicitly to
solve this document management nightmare.

However, this one is a bit too much for my needs - all the client-server
architecture and web-interface and what not. Sometime ago, I was looking
over someone's shoulder and they were using a really neat document
management software. It was on a Mac I think. If you know what it was -
please do let me know!

Level Set Methods and Dynamic Implicit Surfaces

Level Set Methods and Dynamic Implicit Surfaces
Series: Applied Mathematical Sciences , Vol. 153
Osher, Stanley, Fedkiw, Ronald
2003, XIII, 273 p. 109 illus., 24 in color., Hardcover
ISBN: 978-0-387-95482-0

The whole book is available online here.

What does an fMRI signal measure - exactly?

Nikos K. Logothetis is at the Max Planck Institute, in an article in Nature Neuroscience explains in greater detail the relationship between the effect measured by fMRI (the Blood Oxygenation Level Dependent signal), the cerebral metabolic rate of oxygen consumption (CMRO2) and the underlying neural activity

http://www.nature.com/neuro/journal/v10/n10/full/nn1007-1230.html

Matlab codes for active contour without edges (levelset based)

Somebody implemented the algorithm of active contour without edges (based Chan and Vese's paper) in Matlab at http://www.postulate.org/segmentation.php. It runs well, but is slow.

Another Matlab implementation is by Dr. Chunming Li (who is a collaborator of Dr. Kao) and is available at http://www.engr.uconn.edu/~cmli/code/. It is much faster and also has the implementation for multiple levelset functions.

Expectation Maximization

We discussed "Latent Variables Models and Learning with the EM Algorithm" today, mostly using slides from Sam Roweis' talk. The view of EM from the lower bound optimization perspective [1] is particularly interesting and is perhaps the most elucidative view of EM. The discussion in [2] is also very useful to understand extensions of EM. Of course, the canonical reference [3] is always cited and perhaps worth a read if you have a lot of patience.

We will continue the discussion next week when we will discuss the incremental version of EM [2] and revisit Kilian Pohl's work.

[1] Minka, T. (1998). Expectation-Maximization as lower bound maximization. Tutorial published on the web at http://www-white.media.mit.edu/ tpminka /papers/em.html.
[2] Neal, R. M. and Hinton, G. E. 1999. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, M. I. Jordan, Ed. MIT Press, Cambridge, MA, 355-368.
[3] Arthur Dempster, Nan Laird, and Donald Rubin. "Maximum likelihood from incomplete data via the EM algorithm". Journal of the Royal Statistical Society, Series B, 39(1):1–38, 1977

Matlab implementation of level-set methods

A graduate researcher at UCSB has been kind enough to post his implementation of level-set techniques in 2D in MATLAB here
Looks like a good way to start tweaking and learning.

Some more biomedical image analysis software