Two years ago I had a post here (follow-up) where I dared to compare these two programs. The reaction was unfavorable. The ImageJ ticket quoted below seems to indicate that there has been a slight shift.

Wayne [Rasband] had an idea for a command called “Analyze Image” that combines filtering, background correction, segmentation, particle analysis, etc. It would work something like the closed-source, Windows-only  Pixcavator program. As Wayne said, “It would not be an easy thing to create but it would be very popular with ImageJ users. 

I agree.

Prof. Marian Mrozek  was kind enough to inform me about the coming update of CHomP in his email that I quote below:

The power of the software comes from much newer algorithms. Some of them are described in the papers:

• M. Mrozek, P. Pilarczyk, N. Zelazna, Homology algorithm based on acyclic subspace, Computers and Mathematics with Applications, 55 (2008), 2395 –2412.
• M. Mrozek, B. Batko, Coreduction homology algorithm, Discrete and Computational Geometry, 41 (2009), 96-118.
• M. Mrozek, Cech Type Approach to Computing Homology of Maps
  Discrete and Computational Geometry, accepted
• and a few more which are just in preparation.

We just finish[ed] writing a new, much stronger version of the software which will accept not only cubical complexes but also simplicial complexes and general CW complexes and will produce broader output, in particular homology generators, homology maps and persistence intervals for filtered complexes.

The new version of our software at first will be available from the webpage
of our CAPD group at Jagiellonian University, Krakow, Poland:
http://capd.ii.uj.edu.pl/.

Take a look also at our Homology Software page.

RGB stands for Red, Green, and Blue. These are the “channels” in a color image. Each pixel has 3 numbers between 0 and 255 assigned to it.

• (255,0,0) red,
• (0,255,0) blue,
• (0,0,255) green,
• (255,255,0) yellow,
• (255,0,255) magenta,
• (0,255,255) cyan,
• (g,g,g) gray, for any g,
• (0,0,0) black,
• (255,255,255) white

Every color image has three color channels – red, green and blue – and the image features you are after may be more pronounced with respect to one of them.

The channel-by-channel analysis allows one to consider each channel of the color image as a separate gray scale image and analyze them as needed. In Pixcavator just click a button in the Analysis tab for the channel you want.

In the example below, the circles are of pure red, green, and blue. As a result, the red circle which is (255,0,0) becomes 255 in the red channel. But 255 is equivalent to white in this gray scale image. So the red circle disappears in the red channel. Similarly, the green circle will disappear if you choose the green channel, etc.

This option is important for some applications such as microscopy. Different features are sometimes better revealed in different channels. Below is the original image with two clear, to the human eye, features: red walls and green “cells”.

Read about analysis of this image here: http://inperc.com/wiki/index.php?title=RGB_channels.

Q: We need to “measure all the seedlings area (by dividing by the number of them I can get indication to their area and structure, and other parameters I can get)… The area covered is 80*80cm.”

The screenshot shows the results of my experiment. Here the red contours surround the darker areas (vegetation) and green surround the lighter areas inside. So, the total coverage is 60 – 9 = 51%. Unfortunately, I was unable to separate the seedlings from the rest of the vegetation.

There is also work with ecological researchers to measure vegetation coverage but mostly with horizontal shots.

Other examples of image analysis

Q: We “need to verify the internal diameter, external diameter and the wall thickness between the ID, OD and the reinforcement yarn. One issue we have is that the wall is not always concentric. We have a minimum wall thickness specification so we would like to measure the wall thickness at the thinnest point to determine if it meets our spec or not.”  

I analyzed one of the images. I found fairly good contours that capture the inner (red) and outer (green) borders of the hose with the settings that you can see in the screenshot. The measurements for this contours can be seen in the Pixcavator’s output table.

The area inside the red contour is 130,966. Assuming this is a circle, the area is equal to π*R², so the radius is

 R = √(130,966/3.14) = 204 pixels.

Then the external diameter is 408 pixels (one would have to do calibration at this point to convert to inches).

The area inside the green contour is 96,595. Assuming this is a circle, the radius is

 R = √(96,595/3.14) = 175 pixels.

Then the internal diameter is 350 pixels.

This suggests that the thickness of the wall should be 204-175=29 pixels. This is the average thickness of a ring with these measurements. To verify this number one can drop the assumption that these are circles and use the perimeters of the contours taken from the output table. Then

 average thickness
   = (area of the wall)/(average perimeter)
   = (130,966-96,595)/((1,547+1,283)/2)
   = 24 pixels.

A similar computation is presented here: Wall of a blood vessel.

Other examples of image analysis

These are the new features in version 5.0.

• Your choice of settings in the Output tab (the position of the sliders) is preserved when you load a new image to analyze.
• Your choice of color channels in the Analysis tab is preserved when you load a new image to analyze. With these two the user can apply the same settings to a sequence of images if they are similar in nature. So, we get as close as possible to bulk processing without actually creating this complex feature.
• Luminosity is a new color channel that you can choose. It is computed as a combination of the red, green, and blue values: 0.299*R + 0.587*G + 0.114*B. There are four channels now.
• “Display channel” is a new option in the Analysis tab (just like the one in the Output tab). If you have chosen to shrink the image, the shrunken version is shown. This way you can preview all channels and decide which is the best – before committing to time consuming analysis.
• The “Help” menu provides now the links to the help pages of this wiki. The user’s guide and the license are still provided with the program; they are to be found in the “Pixcavator” folder on your hard disk.
• The actual processing time is shown when it’s done, and a beep is produced – but only if processing has taken more than 5 seconds.
• Up to 2000 contours are now shown on the image and their statistics is also displayed. When there are more than 2000 contours, neither is shown.
• A few bugs have been fixed, some remain.

Download here.

A new research paper that uses Pixcavator:

Adsorption Dynamics of CO on Silica Supported Gold Clusters: Cluster Size Effects in Molecular Beam Scattering Experiments by E. Kadossov, U. Burghaus (Department of Chemistry, Biochemistry, and Molecular Biology, North Dakota State University), link, published in Catalysis Letters.

From the paper:

“We report on particle size effects in the adsorption dynamics (gas-surface energy transfer) of CO, studied by molecular beam scattering… the effect of supported nano-size gold metal clusters on gas-surface energy transfer processes (adsorption dynamics)… For the statistical analysis, commercial imaging analysis software (Pixcavator IA 4.2) was used.”

There are nine, to the best of my knowledge, research papers that used Pixcavator and gave credit.

This edition is a version of Pixcavator that comes with a preloaded image. Which means that it’s not really a single program but many – one for each image.

It is a single file “exe” program that does not require any installation. As such it can be used as an alternative to screenshots, for demos etc.

This edition has all the features of the standard edition except for image processing tools. This way you can choose different color channels for your analysis, experiment with the sliders, and save your work.

Most of image analysis examples will soon have links to the corresponding files. For a start, try to run these two examples:

• immunohistochemistry,
• melanoma.

In FF: choose “Save”, then “Run”. In IE: just choose “Run”.

We will also be able to create such files as a service to our customers.

Q: “Immunohistochemistry was performed on my lung biopsies and now I have to analyse my staining. I would like to get a value for the colour intensity of my staining. … In the output of Pixcavator a mean value for grayness is given, but I don’t think all my brown staining is measured. Even if I adjust the threshold for size, not all objects are captured with a red or green contour.  ”

1. The difference in gray isn’t more pronounced because what is displayed is the average not the weighted average. So, one large dark object is outweighed by smaller, lighter ones. If you hover over objects (or look at the table) one at a time, you see a more noticeable difference. You can also save the data as a spreadsheet and compute the weighted averages. Certainly, if the difference is still too small, contrast enhancement would help.

2. It’s better to choose the green channel (or blue) than red. The features are much more distinct. If you click on “Display channel”, you’ll see the difference.

3. You are interested in dark objects only, red contours. So, the light ones only skew the averages. Click “Unmark light”.

4. The “Size” slider does not seem to reveal the features quite well. I used the simple thresholding instead, i.e., the “Intensity, dark” slider.

A screenshots is below. (This example may be somewhat similar: Measuring staining in the liver.)

More analysis here… 

Other examples of image analysis

Q: we need to “count cell/dna etc from still microscopic image… Here we stained DNA …S-phase are in red and G-phase is in Blue. We want to count how many red are there and how many blue are there.”

I used Pixcavator and did a bit of experimenting with the first image. The red cells are easy to capture – in the red channel, 71 total.

Now the red cells are so bright that in the blue channel they are also present. So, here you see both red and blue and have to subtract:

 299 - 71 = 228 of the blue ones.

 Using subtraction here isn’t a perfect solution clearly. To separate red and blue one might try to filter the spots based on the size or intensity.

Image processing can help too – I simply removed the red using Photoshop Elements.

The total was 204. 

One day you’ll be able to use the true color analysis.

See examples of image analysis.

The next version of Pixcavator has just been released (download). The update will help the user to create better image analysis reports with Excel.

These reports are based on the data in Pixcavator’s output table that lists all objects in the image as well as their  measurements and locations. The table, combined with the analysis settings, the statistical summary, and the frequencies of the values in the table, can be saved to hard disk in the form of a spreadsheet. This spreadsheet is a complete report of what has happened.

The frequencies allow one to create a histogram for each column, such as the size, to graphically illustrate the distribution of the values. One can also add images, format the text, etc. The end result may look like this:

For more details read Report generation in the wiki.

A note to our current customers: If you purchased Pixcavator within the last 12 months, i.e., after December 13, 2008, you are entitled to a free upgrade. You can download the new version here and then activate it with your current activation number. If your purchase dates before December 13, 2008, you can acquire the new version here. It goes without saying that the older versions of Pixcavator are to remain active indefinitely.

In the late December the price of Pixcavator will be increased to $245.

From other news, our site Computer Vision Primer has reached 268 articles with over 780 illustrations. The fastest growing category is image analysis examples which has reached 56.

I took this image analysis example from the site of Able Image Analyzer. It was easy to reproduce the results with Pixcavator.

Setting the contrast slider at 100 gives you good contours for both Sicily and the calibration bar. The length of the latter is see to be 217 pixels, so

 217 pixels  = 250 km.

The are of Sicily is see to be 18884 pixels, so

 the area = 18884*(250/217)^2 = 25064 sq km.

(Here 250/217 is the calibration factor). The actual area is 25706, so the error is about 2.5%.

 See other image analysis examples.

Wikipedia’s article lists image analysis software in the form of a table. The columns are:

• Product  
• Developer 
• Cost (USD) 
• Open source 
• Software license  
• OS 
• Continuous 
• Industries, Uses and Applications

I thought that the type of license, open/closed source, OS, etc aren’t very interesting, while some more important, in my view, data should be added. Based on the inspection of the vendors’ sites, I tried to answer the following questions.

What is the price? In the absence of that information, I put $$$$ indicating that the price might be in the thousands.

Is there a free version available for download? Companies with $$$$ usually don’t have that.

Does the site provide examples of how the software has been used for image analysis? Very often, surprisingly little is provided.

Does the site reveal the methods/algorithms behind the software? Commercial vendors say nothing. Open source certainly qualifies for Yes in this category but most of the time the source is all you get. The actual math, algorithms, errors, etc are ignored.

To see the new table follow this link: Image analysis software.

Q. “I was wondering if it is possible to make it as an online service where people could log on to the server and upload their image and obtain the report or csv file.”

A. I have been thinking about a web app for Pixcavator for a while. The idea is certainly very attractive but there are some drawbacks. First, the computations that Pixcavator does are quite CPU and memory intensive. The testing that has been done shows that online computation (short of some kind of supercomputer) wouldn’t be any faster than if it is done on a modern PC. Second, uploading a 1000×1000 bmp file can take as long as the processing itself. Once it becomes common for people to keep their images (and I mean business/research images, not family albums) “in the cloud”, a web application will make more sense. Getting there may take some time but there is no doubt in my mind that this is the future.

These images came from researchers in medical image analysis. They represent “low-intensity multi-spectral image of the tumour in the early stage of development”. Their algorithm has been patented and the results are published in Medical Image Analysis.

The images below came from the same source and show the results of analysis of the first image by means of their algorithm (right) vs. Pixcavator’s (left). The comparison is unfavorable.

I ran Pixcavator myself with the first image, high intensity. I had to move the maximal contrast slider and in about 5 seconds I had a satisfactory result, below.

The low intensity image is here.