image understanding – pattern recognition in imaging ......whereas the term “pattern...

Copyright © 2008 by Lew Brown, Fluid Imaging Technologies, Inc.�

Image Understanding – Pattern Recognition in Imaging Particle Analysis

Lew BrownTechnical Director

Fluid Imaging Technologies, Inc.

I. Introduction

The computational method known as “pattern recognition” has been around for many years now, beginning early in the �960’s with military uses centered upon “remote sensing” (aerial and satellite imaging). Use of these techniques then expanded into the field of medical imaging, machine vision and others. The enormous computational demands of these applications limited early use of the technologies to institutions and organizations that could afford the high cost of the hardware necessary to perform these operations.

The reason for the high cost of use of the technology has been that “pattern recognition” attempts to mathematically duplicate cognitive processes performed by the human eye/brain combination with ease. Indeed, many simple “pattern recognition” operations that we as humans take for granted in our day to day lives are extremely difficult (if not impossible) to reproduce using computational methods. As the cost of computing hardware has dropped precipitously while the performance of this hardware has risen exponentially, it has become increasingly possible for some of the most basic pattern recognition operations to be performed on common, inexpensive computing platforms such as Personal Computers.

Pattern recognition has been limited initially to attempting to uncover or “find” some object(s) within a static image. Applications which involve pattern recognition on moving objects, such as machine vision, have typically required special purpose hardware in order to perform these operations.

This paper will discuss the use of pattern recognition techniques to identify and differentiate different particle types contained in a heterogeneous solution. This application involves imaging microscopic particles in real-time as they flow in a solution, segregating each individual particle as a separate image, and then applying pattern recognition techniques to differentiate the individual particle types. A framework for discussing the complexity of a pattern recognition operation in this application will be proposed, along with some specific examples showing how this framework applies.

“Human Vision” versus “Computational Vision”

As briefly discussed in the introduction, duplicating even simple “human vision” processes that are intuitively simple to us can be an extremely daunting task within a computational system. The human “eye/brain” system is the most powerful computational system known. It is estimated that over 20% of the neurons in the cortex of the human brain (approximately �0�0 total neurons) are concentrated

II.

Figure 1: Most particle analyzers give a distribution of particle size only as shown by the graph on the left. Imaging particle analysis yields size, shape and gray-scale information, enabling the use of pattern recognition algorithms to automatically distinguish different particle types in a heterogeneous sample as shown by the images on the right.

Abstract: This paper will discuss the use of pattern recognition techniques to identify and differentiate different particle types contained in a heterogeneous solution. This application involves imaging the microscopic particles in real-time as they flow in a solution, segregating each individual particle as a separate image, and then applying pattern recognition techniques to differentiate the individual particle types. A framework for discussing the complexity of a pattern recognition operation in this application will be proposed, along with some specific examples showing how this framework applies.

Copyright © 2008 by Lew Brown, Fluid Imaging Technologies, Inc.2

on the task of vision (�). An entire branch of computational research has been devoted to trying to understand and duplicate the functions that the human visual system performs on a regular basis. This area of research is referred to as “Image Understanding”. For the purposes of this paper, we will use the definition contained in the Encyclopedia of Artificial Intelligence by J.K. Tsotos:

“Image Understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the real world scenes that the images represent.” (2)

Image understanding is really a sub-discipline of the broader research area, pattern recognition:

“Pattern recognition is the research area that studies the operation and design of systems that recognize patterns in data. It encloses subdisciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition), grammatical inference and parsing (sometimes called syntactical pattern recognition). Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, person identification and industrial inspection” (3)

As seen from the above definitions, “image understanding” (IU) confines itself to the domain of image processing, whereas the term “pattern recognition” is applied to many diverse fields such as speech recognition and character recognition. As such, image understanding is a better term for the topic under discussion, not only because it is narrower in scope, but, more importantly because it more intuitively describes what is being attempted: namely to “understand” the contents of a digital “image”.

In the introduction, it was also mentioned that pattern recognition techniques have been historically applied primarily to what I will call “needle in the haystack” type problems, where a static image is analyzed to try to “pull out” a desired feature(s). The earliest work in this area can be found in the military, where scanned aerial images were analyzed for features; an example would be “find the tank in the forest”. The objective of this work was to off-load some of the “image interpretation” work classically done by humans in the intelligence community to the computer. By doing this, higher volumes of image data could be analyzed in less time, yielding a sharp increase in the amount of “intelligence” that could be gathered. This type of work was categorized as “remote sensing”, and became more common

when non-classified data sources such as LANDSAT became available. In parallel to this, newer sources for medical imaging such as CT scanners became available, where the same types of techniques could be used; an example would be “find the tumor in the image”.

Just as IU techniques could be used to analyze larger quantities of remotely sensed data, the same workflow could be applied to microscopic images. Instead of presenting microscopic images to a scientist for interpretation, using IU techniques enabled some microscopic interpretation to be automated, for example cell counting. This enables analysis of larger quantities of data, which yields higher statistical significance to any results presented.

Particle Image Understanding (PIU) as described in this paper goes one step further: the image understanding techniques are applied to particles which are flowing through a microscopic system in real-time; the particles being analyzed are not static. This means that thousands of particles are being analyzed in the time that a human observer might be able to analyze, at best, a few hundred under a microscope. Once again, this yields huge benefits in the area of greater statistical significance for the results. Imagine trying to characterize the particle contents of ten gallons of liquid through a microscope: a human would only be able to characterize a couple hundred particles in an hour, whereas the system described here could analyze �00,000 particles in a matter of a couple of minutes. Obviously, a �00,000 particle sample from the ten gallons holds much more statistical significance than 200 particles would. Further, this information is gathered in significantly less time! The distinct advantages of using digital imaging for particle analysis as opposed to other automated techniques (such as electrozone counters or laser diffraction systems) are well understood and documented (4).

The Particle Image Understanding method consists of two distinct steps: first the particles are segregated from the background into individual particle images, then IU techniques are used to extract information from each particle image. Typically the information to be extracted consists of “classifying” each particle into different “types” of particles. This could be as simple as identifying contaminants in a homogeneous sample, or as complex as identifying different types of algae contained in a water sample.

Much of the research into IU has pointed toward parallel processing architectures as being the only possible way to duplicate complex visual processes (5). Since the human visual system is so complex, many diverse areas of research become involved when trying to characterize the


performance of the human visual system, such as computer vision, neurophysiology, neuroanatomy, and psychology (6). To this date, this process is not completely understood. However some basic measurements are postulated: one interesting study estimates a time of around 250 milliseconds for recognition of a simple target in a non-complex background (7). In this example, the observer is given pre-cognitive information on what the target he is looking for is, and the time to process that information is not included. This example also assumes that the eye/brain system architecture is “massively parallel”.

In �988, a workshop was conducted entitled “The DARPA Integrated Image Understanding Benchmark” (8).. This workshop was specifically orientated toward parallel processing architectures, and results were reported for several different existing and purely theoretical computing architectures. The benchmark consisted of identifying several objects in a “2 �/2D” image (one gray scale image co-registered with a “range” image). Even though this was almost 20 years ago, and computing hardware speed was only a fraction of what is available today, the results still yield an interesting statement on the computational complexity of a relatively simple IU task. A commercially available single processor UNIX workstation required �2�.0� seconds to perform the total task. A commercially available 8-processor “mini-supercomputer” required 60.��2 seconds to perform the task. Finally, a Thinking Machines Connection Machine having 64,000 processors (although these machines could be purchased, they were, at the time, considered somewhat “experimental”) was simulated to perform the task in 0.63 seconds (this was not for the “complete” task, only the “low level” portion of it) (9).

One final discussion is warranted concerning pattern recognition in general, which is the difference between “supervised” classification and “unsupervised” classification. In “supervised” classification algorithms, some “a priori” information is supplied to the computer beforehand. This is based on a human identifying “training sets” of data that can be used as a reference for a particular object or image prior to the analysis. In IU, this is manifested by the user identifying objects in an image as belonging to a “class”, and then instructing the computer to “find all the other objects in the image that belong to this class”. By contrast, “unsupervised” classification algorithms, the system is given no a priori knowledge of patterns to look for, instead the computer looks for statistical regularities of data to establish its own “classes”. All of the IU techniques discussed in this paper are “supervised” in nature.

Particle Image Understanding – Levels of Understanding

In PIU, the first step taken is to segregate particles from the background (fluid containing the particles) in the image. This is done using a simple “gray-scale thresholding” operation: a “threshold level” of gray scale is set by which the particle is extracted from the background. A digital camera takes an image of the field of view of the microscope, which divides the field of view into pixels (the number of pixels in the field is determined by the camera’s resolution). For each pixel in the field, a gray-scale value is recorded which corresponds to the intensity (for simplicity, we will limit this discussion to a monochrome camera; a color system works the same way except that it records a red, green and blue value for each pixel). In a typical digital camera, the gray-scale is measured as an 8-bit number, which represents 256 discrete levels of intensity. In the thresholding process, each pixel’s gray scale is compared to the normal background (fluid only) level, and if the difference exceeds the threshold value, the pixel is counted as a “particle pixel”. This creates a “binary” representation of the original image, where each pixel is classified either as “background” or “particle”.

The binary image is then “scanned” by the software to group together adjoining pixels that have been classified as “particle”, which creates groups of pixels representing each particle. It is important to note that for this technique to work properly, the particles must be in a solution that is dilute enough so that each particle is physically separated from the others when presented to the microscope in the fluid. Otherwise, multiple particles will end up being “grouped together” by the algorithm as one image. The final step is to “cookie cut” each particle out as a separate image to be measured and stored. It is important to note that while the binary image is used to segregate the particles from the background, the full gray scale image of the particle is what is actually stored.

Two different types of measurements can be made from each particle image: “spatial” and “gray-scale”. “Spatial” measurements such as length, width, perimeter, etc. are carried out on the binary thresholded image, which greatly increases the speed at which the measurements can be made. “Gray-scale” measurements such as transparency and sigma intensity are obviously calculated using the full gray scale image. Since the image is stored in gray scale, it can later be viewed by a human observer for subtle features or classification which might not be possible via machine-based pattern recognition. It has the added benefit of being a permanent record of each particle, so that unexpected (or even expected) results can be studied after the fact; in

III.


other words, the images represent an “audit trail” for verification of the automated results.

One final note that has to be considered is the “spatial resolution” of the system; this is a measurement of how many pixels correspond to a unit area (usually expressed as pixels per unit length, assuming the pixels on the sensor are actually “square” in geometry). The more pixels “covering” each particle, the more detail that is captured for the particle image. The more detailed the image in terms of spatial resolution, the more information and precision the measurements are associated with the particle. A simple example is shown in Figure 2.

With this as a backdrop, I propose a system for different “levels of particle image understanding”:

Level 0 (Figure 3)

At this level, the only measurement that can be made is whether a particle is present or not. The only data that can be gathered at Level 0 is a particle count or concentration.

Level 1 (Figure 4)

We begin to get more information gathered at this level. On the spatial side, we can now both “count” and “size” the particles. It is important to note, however, that at this level we have a severe restriction placed on the spatial data due to an assumption that all particles are “spherical” in shape. The “size” of the particles is expressed as an Equivalent Spherical Diameter (ESD), which can be thought of as “scrunching” the particle down into a sphere and then calculating its diameter. In imaging particle analysis, this is done by taking

the area of the thresholded image and reporting the diameter of a circle of equal area. Other particle analysis techniques use different measurement methodologies than imaging does (usually volumetric data), but the end result is the same due to the assumption of the particles being spherical. This is where these techniques stop, however, because they can only produce “count” and “size” (based on ESD).

In the imaging particle analysis system, we now can add in gray-scale attributes such as average intensity and transparency which give us more information about the particle. These gray-scale measurements are unique to imaging particle analysis. As we will see later on in the discussion, the number

The Effect of Resolution

Theshold

Gray-Scale Image

Binary Image

Resolution: 1 pixel = 1 unit area Resolution: 4 pixels = 1 unit area

Size (ESD) = 2√(4 pixels/π) = 2.26 units

Size (ESD) = 2√(2 pixels/π) = 1.60 units

Size (ESD) =2√[(12 pixels/4)/π] = 1.95 units

Size (ESD) =2√[(6 pixels/4)/π]= 1.38 units

Figure 2: The particles on the right are being sampled at 2X the resolution of the particles on the left. Note the increase in detail within the binary (thresholded) images on the right. Also note that the size gains more accuracy with the added resolution. “Size” is based upon the Equivalent Spherical Diameter (ESD), which is calculated as follows: ESD = 2√(Area/π)

Level 0

Theshold

Gray-Scale Image

Binary Image

Spatial Information Gray-Scale Information

N/A

N/A

Particle Present Particle Not Present

Attributes Captured“Count” Only!

Figure 3: Level 0 Particle Image Understanding


of unique measurements that can be made for each particle greatly affects how discriminating the pattern recognition algorithm can be. More data points (measurements) per particle allows more subtle discriminations to be made amongst different particle types numerically.

Level 2 (Figure 5)

At Level 2, we begin to gather “morphological” information on the “shape” of the particle by now measuring the particle’s length and width. No longer is the assumption being made that a particle is “spherical” in shape. We are no longer limited to a “size” measurement based upon

ESD (although this measurement is still made). You may be noticing that as we go to higher levels of image understanding that more spatial resolution is required for the higher level measurements. This was hinted at earlier, but becomes quite clear when looking at these diagrams of the various levels of image understanding.

Level 3 (Figure 6)

At Level 3 and higher, the increased spatial resolution enables us to add much higher-level morphological attributes to the measurements made. For example, the “circularity” of a particle can now be described by measuring the actual perimeter of the particle and comparing it against the

“theoretical perimeter” for a spherical particle having the equivalent ESD.

As we will see later, the more discrete measurements that can be made for each particle, the more information available to the pattern recognition algorithms allowing more subtle differentiations between different particle types. As discussed previously, however, the higher-level measurements require higher spatial resolution on the sample (for the spatial measurements especially). This is a key limitation of the imaging particle analysis technique. Without going into a detailed discussion of sampling theory and diffraction

limitations, suffice it to say that this technique really does not allow for the higher levels of image understanding for particles below 2 microns in diameter.

To understand this better, consider the following: for this system, the highest spatial resolution available is around 0.25µ/pixel. If we look at imaging a �µ ESD sphere, this means that the sphere will be captured in a 4x4 pixel square. Because of this, we can realistically only expect to get Level � (count and size) information from the image. We simply need more pixels in order to get the data needed to reliably gather the data necessary for higher levels of PIU. More pixels covering the object are

Level 1

Theshold

Gray-Scale Image

Binary Image


Particle PresentSize =2

Particle PresentSize =1

Attributes CapturedCount, Area, Size (ESD)

Particle PresentSize =2Avg. Intensity =100Transparency = 0.2

Particle PresentSize =2Avg. Intensity =150Transparency = 0.4

Attributes CapturedCount, Area, Size (ESD), Avg. Intensity, Transparency


Level 2

Theshold

Gray-Scale Image

Binary Image


Particle PresentSize =2 Length =3Width = 2Aspect Ratio = 0.67

Particle PresentSize = 1.4Length = 3Width = 1Aspect Ratio = 0.33

Attributes CapturedCount, Area, Size (ESD), Length, Width,

Aspect Ratio

Particle PresentSize = 1.4Length = 3Width = 1Avg. Intensity =100Transparency = 0.2

Particle PresentSize = 1.4Length = 3Width = 1Avg. Intensity =150Transparency = 0.4

Attributes CapturedCount, Area, Size (ESD), Length, Width, Aspect Ratio,

Avg. Intensity, Transparency



always better, but because of the limitations of optical microscopy, we can only get more pixels on larger objects. For this reason, we are being realistic in saying that higher levels of PIU can only be obtained when looking at particles which are larger than 2µ in ESD.

Pattern Recognition Applied to Particle Image Understanding

Once the data is captured by the PIU system (step � of the process), we are now ready to attempt “classification” of the data. The system used for this paper is the FlowCAM®, manufactured by Fluid Imaging Technologies of Yarmouth, ME. After the FlowCAM has acquired the data, we now have two sets of files which have been gathered: each individual particle image is stored in a TIFF file, with each particle having an associated row in a spreadsheet file which corresponds to all of the measurements made on that particle. The VisualSpreadsheet© software includes a third proprietary file, which references each particle image to the corresponding row of data for that particle in the spreadsheet. This enables the ability to look at any particle image and automatically view a readout of all measurements associated with that particle.

VisualSpreadsheet operates just as any other spreadsheet does; it can perform sorting and filtering operations on the data. The difference is that rather than interacting with the tabular spreadsheet itself, the operator queries, sorts and filters the data via the images, with the results of any operation being displayed as the particle images themselves as opposed to thousands of rows of numbers only. At any point during these operations, the user can view the tabular data generated by a sort or filter as a summary of statistics (means, standard deviations, Coefficient of Variability (CV), etc.) while also observing all of the images associated with this data.

The two types of classification that can be performed in VisualSpreadsheet are “value filtering” and “statistical filtering”. Both of these will be described in more detail below, but it is important to remember that both of these processes represent “supervised classification”. This means that the operator will provide “a priori” knowledge to the system beforehand on exactly what we are looking to classify.

IV.

Value Filtering

Value filtering is the type of filtering most spreadsheet users are familiar with. The user inputs values (or ranges) for a given variable(s) (column(s) in the spreadsheet), and then the computer “filters” all of the records (rows in the spreadsheet) to find only those records that meet the filter criteria defined by the user. As a simple example, one could query the spreadsheet to find all records which have a particle diameter (ESD) between �0 and 20µ, and the results would be only those particle records that meet this criterion. Because the FlowCAM can record up to 26 different measurements for each particle, very complex queries can be built to look for specific particle types. For example, one could create a filter to find large fibers in a sample by querying on “aspect ratio (w/l) <0.2”, “length >200µ”, and “transparency <0.4”. The more subtle the distinctions needed to be made, the more variables that can be filtered on. This is done via the dialog box shown in Figure 7.

With 26 variables to choose from, one can obviously build up very specific filters, but this requires a lot of user interaction and understanding of what variables are best to use in order to classify a particular particle type. This is where VisualSpreadsheet provides a far more intuitive method for accomplishing the same end result: the user merely selects a particle (or group of particles) and instructs the software to “filter like selected particles”. What this does is to automatically build a value filter containing the data ranges for each particle attribute contained in the selected images. By default, this fills in the value ranges of all available variables (particle measurements) for the selected particle images (the “training set”). If desired, the user

Level 3

Theshold

Gray-Scale Image

Binary Image

Spatial and Gray-Scale Information

Attributes CapturedCount, Area, Size (ESD), Length, Width, Aspect Ratio, Area, Circularity, Elongation, Perimeter, Convex Perimeter,

Roughness, Avg. Intensity, Transparency



can “fine tune” the search by editing parameter ranges, excluding parameters or applying percent tolerances to the ranges. For instance, if one were looking for a very specific particle shape, but knew that these particles could be present over a very broad size range, one would merely exclude the size parameter from the value filter.

Statistical Filtering

While “value filtering” can produce some very impressive results, it still has some limitations. For one, it requires that the user have some knowledge of just exactly what variables can be best used to “pull out” the type of particle one is looking for. It is further limited in that it makes a straight “AND” comparison amongst any variables selected. In other words, if any particle’s data value falls outside the given range on any measurement, then it will not be considered as part of the “class” even if all the other selected variables fall within the given ranges. So, in the end, the classification is purely binary; either the particle fits within all the ranges provided (and is classified as a class member) or it does not.

Statistical filtering overcomes these shortcomings by using statistically based “weighting” on each variable to decide how much emphasis should be placed on each particle measurement. To reuse the brief example discussed above, if we are looking for a certain particle shape that may be present over a broad size range, statistical filtering will determine that the “size” (ESD) of the particle is not very significant, and therefore “weigh” that variable very low compared to other variables which show a tighter range in values derived from the training set particles. It is critical to note that the degree of success with either value or statistical based filtering is very dependant upon the user wisely choosing the “training set” particle images. In the above example, if the user chose training set particles with the same shape, but also around the same size (ESD), then the algorithm would decide that size (ESD) was an important variable, and would only find particles of the desired shape within a narrow size range!

This is really a critical point: human knowledge and input is still the most valuable contribution to the process. Someone who really understands the characteristics of the particles we want to find has to define an optimized training set to best “pull out” the particles that belong in the class. This is the “supervised” part of “supervised classification”! The good news here is that once an optimum training set has

been defined, it can be saved and used to classify other data sets containing the particle type (class) we are looking for. In fact, this is desirable, because if we use the same training set (called a “library” in VisualSpreadsheet) for other populations, then we are always making the same statistical comparison, thereby increasing the statistical confidence in the classification showing differences between different samples of the same fluid.

Without going into a detailed discussion of the statistical methods employed in statistical filtering, let us briefly describe how it works. Basically, the “training set” is used to generate statistics (such as mean, standard deviation and CV) for each data variable being used. In the case of PIU, these data variables are particle measurements such as ESD, length, width, “circularity”, “transparency”, and average intensity (up to 26 of these measurements are generated for each particle in the FlowCAM). For each training set (or class), the statistics generate a “normalized” point in a multidimensional space representing the class. The data has to be “normalized” due to the fact that each measurement has different ranges of potential values (and units), and this normalization allows for each variable to be evaluated equally. At this point, when the classification is run, each

Figure 7: VisualSpreadsheet “Value Filter” Definition Dialog


particle image has its own normalized point plotted in the same multidimensional space, where it can be compared against the points previously defined for the training sets, and the similarity of the incoming particle to each of the target classes can be determined. While there are literally thousands of different classification algorithms published, the one used most typically is called a “Euclidean Distance” classifier. The distance in the multidimensional space from the sample particle’s point to each of the classes’ points is calculated, and the class which is closest (“minimum distance”) to the target particle is assigned to it. As a gross oversimplification, consider the illustration shown in Figure 8, where a target point’s distance between two classes is calculated.

Recall that in “value filtering”, a “binary decision” is made that the target particle either belongs to or does not belong to a class. In “statistical filtering”, we instead calculate the probability of a target particle belonging to a class; essentially this now allows for “gray area” decisions” where a particle “may” belong to a class. First we calculate the “minimum distance” of the target particle, which tells us that this particle is most likely a member of the class having the minimum distance to the target particle. But

the distance to this class gives us a relative measure of how similar the target particle is to the class selected. If the distance to the class is very short, then it is much more likely to actually be a member of that class as opposed to if the distance is very large! The algorithm establishes the degree of similarity to the class by assigning a “normalized score” to each particle based upon this distance. A particle with a score of � is definitely a member of the class, while a particle with a higher score (say 9) is probably not a member of the class. Since the measurements are normalized, the filter score “cut-off” for being a member of the class will vary depending on how “tight” the distribution of the library particles are. A “tighter” distribution will yield a lower filter score cut-off number.

In VisualSpreadsheet, when a statistical filter is run, each particle is assigned this “score”, and the results are then displayed by showing the particle images sorted in ascending order by the filter score. A statistical tolerance is defined for the score, which says that all particles with a score of “X” or lower are determined to belong to the class. These images are “highlighted” with a red box, and all other images are not. We can then visually inspect the results by looking at the images, as shown in Figure 9.

At this point, the user can interactively edit the classification to include particles which were not selected or remove particles which were selected from the class. This is generally only necessary in a very sparse sample where we need to actually count (enumerate) individual particles in a class. An example of this would be a water analysis where a particular algal species of interest might be present in a very

AspectRatio

(width/length)

Diameter (ESD)

Class “A”

Class “B”

“Target Point”D1

D2

D1<D2Class “A” is closer to target point, so target point belongs to “Class ”A”

Figure 8: “Nearest Neighbor” calculation for a “target point” and two “classes”

Figure 9: After statistical filtering, particles below the “filter score” are highlighted in red. Note that particle #9121 is the first non-selected particle, as its filter score is 8.09, which exceeds the statistically determined cut-off for membership in the class.


low concentration (�0’s of particles/ml). In most applications, where we are merely interested in the relative concentrations of different particle types, we can accept the results of the statistical filter without any further interaction. This is because a variation of a few particles out of thousands will not be statistically significant. Also, it is important to remember that if we apply the same statistical filter to multiple samples, the results are statistically normalized because we are using the same “library” (training set) for the calculations.

Examples of Particle Image Understanding

The following two examples will be used to illustrate Particle Image Understanding in practice. The first is a relatively simple example where the object is merely to determine the concentration of a single type of particle in a heterogeneous sample, whereas in the second example we will be trying to actually enumerate the quantity of two different particle types in a single sample.

Example 1: Quantifying Sugar in Chocolates

In this example, the object was to quantify the amount of sugars contained in chocolate samples. As can be seen in Figure �0, the chocolate contains many different types of particles. However, the larger “crystalline” particles are known to be sugars. Because the sugars have a very distinct appearance and are in a relatively narrow size range, this is an example where a “value filter” will work quite well.

A library (training set) of sugar particle images was built first, and then used as a value filter to quantify the amount of sugar found in multiple samples. VisualSpreadsheet enables the user to directly use any library for either value or statistical filtering. When used as a value

V.

filter, the software can also perform the filtering “on the fly” during acquisition, displaying the filter results as an additional summary statistic both during acquisition and afterward.

Figure 10: Typical results for a FlowCAM run on a chocolate sample. 20,000 particles were imaged, stored and measured. The summary statistics appear in the left hand window, with particle images displayed in the right hand window. Note the diversity of particle size, shape and transparency.

Figure 11: Results of the value filter classification on the “common milk chocolate” sample. Out of 20,020 particles measured, 319 of them were classified as “sugars”, representing a Volume Percentage of the sample of 11.61%. The “Library” window shows the particle images used for the training set.

Copyright © 2008 by Lew Brown, Fluid Imaging Technologies, Inc.�0

The first sample run was a “common milk chocolate” and the results of a typical run are shown in Figure ��. For each sample, multiple runs were made in order to check for repeatability in the results. The results were found to be very repeatable.

The second sample run was a “premium dark chocolate”. Once again, multiple runs were made in order to check for repeatability. The same library of particle images was used to perform the value filtering that had been used on the previous sample. As noted previously, the use of the same library particles for the filter on each sample insures that the same particles are being searched for in both samples. In other words, the same statistical comparisons are being made on both samples. The results for a typical run with the “premium dark chocolate” sample are shown in Figure �2.

In comparing the results of the two samples, one sees a volume percent composition of the “common milk chocolate” of ��.6%, as opposed to a volume percent composition of 7.25% for the “premium dark chocolate”. These results agree with a “qualitative taste testing” of the two chocolates: the milk chocolate is higher in sugar content than the dark chocolate, which leads to it tasting “sweeter”.

Although this example involves food, the same technique is applicable to any type of sample one can imagine. For instance a chemical manufacturer will want to know the individual percent content of a number of different particle

types contained in a mixture. Another example would be quantifying the amount of oil contained in water that may have many other particle types present, such as in the petrochemical industry when evaluating “produced water”.

Example 2: Enumerating Algae in a Drinking Water Supply

In this second example, the goal is to actually quantify (enumerate) the amount of two different algae types contained in a water sample obtained from a public surface water supply. Some algae can cause noticeable “taste and odor” issues within drinking water, and are therefore undesirable. If these algae go untreated and “bloom”, residue will end up at the consumer’s tap, causing complaints and possibly panic. If the bloom is allowed to go this far, a major

quantity of chemicals will be required in the reservoir to remove the algae. However, if the algae’s presence can be detected prior to a bloom, a small amount of treatment will prevent the bloom. This saves money for the utility and prevents unnecessary complaints.

To prevent this from occuring, the utility will take daily samples from the reservoir for analysis. In the past, these samples had to be examined under a microscope by trained technicians to enumerate the different species of algae present. This is not only expensive and time consuming, but also only allows for small amounts of sample to be analyzed due to the time required to manually perform the enumeration.

The FlowCAM is succesfully being used by a number of water utilities to automate this process using statistical pattern recognition techniques. Figure

�3 shows a screenshot of a typical water sample after data acquisition in the FlowCAM. Water samples tend to be extremely “sparse” by nature, having a very low number of particles per unit volume (in this case �708 particles/ml). Because of this, using manual techniques with a microscope is very time consuming due to the fact that only a very small amount of sample can be viewed at a time. Therefore, the quantities that can be analyzed manually usually do not represent a good statistical sample.

This is a perfect example where automated imaging particle analysis using statistical pattern recognition can offer

Figure 12: Results of the value filter classification on the “premium dark chocolate” sample. Out of 20,026 particles measured, 118 of them were classified as “sugars”, representing a Volume Percentage of the sample of 7.25%.

Copyright © 2008 by Lew Brown, Fluid Imaging Technologies, Inc.��

immense savings in time and money. The technique is very starightforward: libraries (training sets) are built for each species of algae it is desired to enumerate, and then statistical pattern recognition is used to automatically quantify the amount of each species of algae within each sample. Remember that once these libraries are built satisfactorily (by a trained expert), they are saved and used as the basis for comparison on every incoming sample. The automatic analysis does not require an operator with any specific knowledge of algae identification once the libraries have been built.

Figure �4 shows a screenshot of the water sample with example images of the two types of algae we are interested in quantifying, Asterionella and Tabellaria. You will note that these two types of algae have very similar sizes, transparency, and other characteristics. It would be very difficult to construct a simple value filter that could easily distinguish between these two particle types.

Statistical pattern recognition, however, can be used to discriminate between the two different algae in a very straightforward manner. First, two sets of libraries (training

sets) are built by an expert who can identify good examples of each type of algae, similar to the ones shown in Figure �4. At this point, the statistical filter calculates the point for each class in a multidimensional space using all 26 variables collected by the FlowCAM. Finally, each particle in the sample is compared in this multidimensional space against the two libraries and scored against each. Each particle is then assigned to the nearest class, but only if its filter

score is less than the confidence score determined by the filter. The remaning particles are left as “unclassified”. Figure �5 shows the results of this automated classification.

Although the two classes of particles found in this example may be easy to distinguish by the human eye/brain (as seen in Figure �4), this is computationally a fairly advanced discrimination to make mathematically. The fact that the FlowCAM records 26 different measurements for each particle gives the statistical filter the amount of data necessary to make such a subtle discrimination automatically. In this example, it makes it realistically possible to analyze enough sample to give statistically significant results in a very short period of time. Such an analysis performed manually by humans

Figure 13: Overview of results from FlowCAM acqusition of particles from a water sample. Note the “sparseness” of the sample; it contains only 1708 particles/ml. Also note the diversity of different particle types found in the right hand window.

Figure 14: Water sample showing the two types of algae that need to be quantified: the upper right window shows Asterionella, and the lower right window shows Tabellaria. These two types of algae are very similar in size and transparency, so can not be as easily distinguished automatically.

Copyright © 2008 by Lew Brown, Fluid Imaging Technologies, Inc.�2

counting through a miroscope would be time and cost-prohibitive to perform on a regular basis.

Conclusions

The two examples detailed above show how statistical pattern recognition can be used to automatically differentiate and quantify unique particle types contained in a heterogeneous sample. The more subtle the distinctions being made, the higher the level of Particle Image Understanding required in order to make the distinction. Example � only required a simple Level 2 value filtering in order to quantify the sugars in the chocolate, whereas Example 2 required a Level 3 statistical filter in order to distinguish and quantify the two different algae types of interest.

These types of analyses may seem quite simple to the human eye/brain system, but are quite complex to accomplish automatically using mathematics in a computer. However, as we can see, this type of mathematical analysis is now within the realm of possibility to perform on common personal computers using off-the-shelf software. The key to performing this type of automated analysis is having an image acquisition system capable of producing the amount of different measurements required to make higher level Particle Image Understanding segmentations. Finally, the ability of such a system to collect enough particle images to produce statistically significant sample quantities will enable the use of these techniques in applications where performing manual analysis through a microscope would be cost and time-prohibitive.

References

�.) Kandel, E. & Schwartz, J. (Ed.) (�98�). Principles of Neural Science. New York: Elsevier/North Holland.

2.) Tsotsos, J.K.. Image Understanding. From Shapiro, S. C. (Ed) (�987). Encyclopedia of Artificial Intelligence. New York: John Wiley & Sons.

3.) Association for the Advancement of Artificial Intelligence web page (http://www.aaai.org/home.html).Description of Pattern Recognition research area at University of Delft (http://www.aaai.org/AITopics/html/pattern.html).

VI.

VII.

4.) Brown, L. (2004). Continuous Imaging Fluid Particle Analysis - A Primer. Fluid Imaging Technologies White Paper (http://fluidimaging.com).

5,6.) Tsotsos, J.K. How Does Human Vision Beat the Computational Complexity of Visual Perception? From Pylyshyn, Z. (Ed.) (�988). Computational Processes in Human Vision: An Interdisciplinary Perspective. Norwood, NJ: Ablex Press.

7.) Duncan, J., Ward, J., Shapiro, K. (�994). “Direct Measurement of Attention Dwell Time in Human Vision”. Nature 369, 3�3 -3�5. New York, NY: Nature Publishing Group.

8,9.) Weems, C., Riseman, E., Hanson, A. & Rosenfeld, A. (�99�) “The DARPA Image Understanding Benchmark for Parallel Computers” From Journal of Parallel and Distributed Computing ��, �-24. Amsterdam, NL: Academic Press, Inc. (Elsevier).

Figure 15: Results of the automated statistical classification. The “Clasify” window shows the particles identified as members of each class. Note there are two “tabs” in this window, one for each class. In this case the window shows the particles classified as “Asterionella”, but clicking on the “tab” labelled “Talaberia” would show the particles classified as that type. Particles in the right hand window are the particles left over as “unclassified”. Note also in the lower part of the left hand window that exact statistics (including count and concentration) for each class are summarized.

image understanding – pattern recognition in imaging ......whereas the term “pattern...

Documents