Better AI in Image Analysis Software?

J.P. Duke asks: "There is an excellent research article published by the Mayo Clinic in the J Ortho Sci that compares two common software-based approaches in analyzing scanned protein gels. Among other conclusions, they found that the two most popular applications for this research had different tendencies in quantifying proteins -- and that differences in AI algorithms show clearly different results for proteins that are less-separated on gels. This implies that much major scientific research that depends on these tools might be suspect to flaws very early in analysis. Being a cancer researcher at a large research institution, much of my work depends on software being able to accurately analyze scanned images of protein gels in which proteins are simply displayed as spots on the gel. Among other things, the software needs to be able to precisely calculate the density of protein in a spot as well as the number of actual proteins contained in a spot. What we choose to investigate further as potential biomarkers for cancer depends heavily on the ability of the AI built into these applications." Exactly how far has image-based AI improved in the last several years? Might some of those improvements help someone in J.P.'s situation? "My questions for Slashdot are as follows:

• Overall, how good has research image software AI become in recent years? Have there been any key software or mathematical breakthroughs that have substantially increased the 'intelligence' of software? How far along is this technology?
  
  
• Based on your knowledge of software, what are some things researchers can do to help the software better do its job? For example, using a high quality scanner at higher resolutions generally helps results. What other things can be done to promote better results?
  
  
• Finally, all applications that I know of in this area are expensive commercial solutions. As the companies that produce the applications are for-profit, the algorithms and technology used are completely closed and proprietary. Thus it is hard to understand what the software is really doing. Does anybody know of any open source (or at least 'open algorithm') solutions? Even if they are inferior at this point in time, being able to clearly understand what the AI is doing makes us better off in several ways.

Thank you all for your help, it is greatly appreciated."

Comments

AI might not be the proper term...

[ComputerSlicer23]

I've worked with several people who are grad students or professors in this area. The first thing you'll probably want to do when looking for research, is use the term "expert systems", possibly in place of "Artificial Intelligence", possibly in conjunction with it. Full Disclosure: I don't know anything about this area really, they just used me as a sounding board to practice presentations, or get ideas about how to tweak an algorithm or two.

My former boss was working on his Master's Thesis, what worked on recongizing shapes based on edge boundary analaysis (among other things as I recall). He worked with the professor who as an expert in "Artificial Intelligence". However, they generally referred to the types of work my boss was doing as "Expert Systems", not as "Artificial Intelligence".

Kirby

Amazing....

[zappepcs]

This is amazing for several reasons. First, I think I'd get fired for letting software (that may or may not be working correctly) do a job that is so important and not have any humans checking the work.

Second, AI in general has been smoke and mirrors from the start, in all of its generalized forms. Its amazing that there is little to show for this particular sci/tech branch of engineering after so many years and attempts.

Currently, there are tons of people investigating how the human (and animal) brains work to better understand 'intelligence' in order to create a better AI. Everyday, if not on /., on other news lists, there are little news stories of some observation or breakthrough in that area.

So the answer is that yes, AI is coming along, and specifically computer vision. You can google it yourself. From the DARPA Grand Challenge to NASA and many other ventures, computer vision is being improved. The more improvement there is for computer vision, the better the algorithms can get for recognizing protien smears on a picture.

I think that you will find there are people who are not only using visual scanning, but compiling this with IR and other types of scanning to better analyze the material.

Computer based vision analysis is everywhere around you. The airline industry uses robotic scanners to look for structural defects in planes by scanning every mm of the surface in several ways. This is done mostly by computers.

Mining and geologic communities are putting robots with computer vision and scanning software to work to find thing that is just impossible by the human eye. Say a robotic helicopter flying over a mountainous area scanning for fire prone areas using IR, sonic and other types of scanning.

The oil industry has been using image analysis for years to find better oil sources in the earth.

This type of stuff is all around us. Finding F/OSS sources of it is perhaps just a matter of scanning for it. Better yet, when you find some, put out some payola to support their efforts. There are open source computer vision projects. Intel has made efforts to support this among others.

Electronics manufacturing is using it as well. I think that if you can focus some funds toward the right group, they will have the tools to develop the specific types of image analysis that you require for your industry.

I imagine that scanning protien smears is not much more difficult than finding micrometer sized fractures in the skin of an airplane, or finding hard to see stars using amature telescopes and computer driven camera technology.

Spend some time with your new friend Google.

Re:Amazing....

[Hast]

The problem with "AI" as a term is that it is pretty much all encompassing. It's a bit like Theory of Everything amongs physics.

But I wouldn't say that research has been futile though, far from it really. The thing is that ever since humans began thinking about intelligence we have formed new hypothesises about how it works. However it really wasn't until we started to try and replicate the effect in a "dumb" system that we really got to the bottom of things. In the early days everyone was confident that we'd soon have this AI thing working. Since they we have discovered a lot of things which are not AI which we previously thought would be AI. (Things like chess computers, expert system and so on.) It turns out that most of these are just different (elaborate) ways to search.

While that knowledge hasn't really given us any AI computers, we have gained a lot of knowledge about how intelligence works. (Or rather, how it doesn't work.)

I have worked a bit with computer vision, image analysis and AI; I think what you really want to look for is computer vision or image analysis tools, not "AI". In my experience if anything has "AI" in the title it's likely bogus marketing at work - buyer beware!

When I was taking classes in this area some interesting work wrt cancer was done by multispectral imaging tools. This basically mean that you use images that are taken outside the range of our human vision to look for things. This is one quite efficient way to find things that are very hard or impossible to find in normal pictures.

My advice to the original poster is to look for help at technical universities in your area. I know that the university where I studies they cooperated with research hospitals and other medical places to find new ways of doing this type of things. You could also look around for companies that does this sort of thing and start examining what they have.

some suggestions

[rensci]

Overall, how good has research image software AI become in recent years? Have there been any key software or mathematical breakthroughs that have substantially increased the 'intelligence' of software? How far along is this technology?

The problem is not a lack of intelligence, it's a lack of documentation, reproducibility, calibration, and statistical validity.

Based on your knowledge of software, what are some things researchers can do to help the software better do its job? For example, using a high quality scanner at higher resolutions generally helps results. What other things can be done to promote better results?

While higher resolution scans are generally a good thing, the don't necessarily increase the accuracy or validity of the results (and could even decrease it, depending on what the software does).

Until you get better software, you simply can't trust the measurements blindly: you have to go over spots that are important to you manually and possibly carry out measurements by hand. Other conceptually simple things you can do is compare the results from using multiple image analysis packages, multiple scans at slightly different settings and resolutions, and repeating the experiment itself multiple times; results that are consistent across those conditions are more likely to be "real" than results you get from a single analysis.

Finally, all applications that I know of in this area are expensive commercial solutions. As the companies that produce the applications are for-profit, the algorithms and technology used are completely closed and proprietary. Thus it is hard to understand what the software is really doing. Does anybody know of any open source (or at least 'open algorithm') solutions? Even if they are inferior at this point in time, being able to clearly understand what the AI is doing makes us better off in several ways.

Well, there are quite a few published algorithms for this problem, and many of them have been implemented in open source form. Many of them work well at identifying and quantifying visually obvious, isolated spots, which is what they were designed for, but there is little reason to believe that they give meaningful results when spots are fuzzy and/or overlapping. There are some methods that potentially can quantify overlapping spots, but validating such methods is difficult and I doubt that the commercial packages have done this.

I work in an academic research group working on finding and precisely quantifying fuzzy spots in another domain (and we are planning on releasing our software fully documented and in open source form); quantitative analysis of gels would be another possible application. If you like, let me know your contact information and I'll get in touch with you.