Measurarty

Measurarty is the evil player in the game of Cellock Holmes and WatsOn. Measurarty is the pathogen detection logic behind our project. Using our Measurarty algorithm, we want to automatically detect pathogens from the chip photos delivered by WatsOn, without human interaction. Besides reducing the risk of human errors, this makes our device usable by almost everyone.

Measurarty - An Introduction

Our device control software is able to take images of incubated chips inside WatsOn. Yet, that does not bring the user closer to the answer of the question:

In fact, answering this question seems trivial for a human: Just check whether a colony grown has grown on the chip and you're done. This task is even easier with our chip system, because these show fluorescence wherever a pathogen has been detected.

But is this an easy task for a computer? Actually not. The task of automatic detection is tried by several disciplines in computer science, from pattern recognition over machine learning to by medical imaging chairs.

Here, we would like to present a pipeline for this task that makes use of easy segmentation and classification algorithms. First, Measurarty segments the target image using Statistical Region Merging (SRM) in order to find regions of similar properties. After this step, we can segment the picture using histogram thresholding in [http://en.wikipedia.org/wiki/HSL_and_HSV HSV color space] to find candidate regions for pathogens. Finally, a classification algorithm can detect the pathogen on our chips.

Statistical Region Merging (SRM)

Before briefly introducing Statistical Region Merging (SRM), we would like to explain why we need this step, and why this algorithm is an ideal choice.

Compared to other clustering algorithms, SRM is quite leight weight, yet delivers deterministic results and is not dependent on a certain seed (like k-means, for example).

On the other hand, it can create as many refinements as one wants and is thus flexible enough for the our purposes. Finally there's already been knowledge about this algorithm in the group.

Statistical Region Merging (SRM) (Nook and Nielson, 2004) is a clustering technique also used directly for image segmentation. A region $R$ is a set of pixels and the cardinality $\lvert R \rvert$ determines how many pixels are in one region. Starting with a sorted set of connected regions (w. r. t. some distance function $f$), two regions $R$ and $R'$ are merged if the qualification criteria $\vert \overline{R'}-\overline{R} \vert \leq \sqrt{b^2(R)+b^2(R')}$ with $b(R) = g \cdot \sqrt{\frac{\ln \frac{\mathcal{R}_{\lvert R \rvert}}{\delta}}{2Q\lvert R \rvert}}$ is fulfilled. Therefore, $\mathcal{R}_{\lvert R \rvert}$ is the set of regions with $\lvert R \rvert$ pixels. Typically $Q$ is chosen as $Q \in \lbrack 256, 1\rbrack$ and $\delta = \frac{1}{\lvert I \rvert^2}$.

The $Q$ parameter mainly influences the merging process. For an example, see the figure SRM Regions below. The lower the chosen value for $Q$, more coarse the regions become. Using a union-find structure, the segmentation does not need to be recalculated for each $Q$ level. For the step from $q$ to $\frac{q}{2}$, just the qualification criteria needs to be applied to the regions from the $q$ result. A MATLAB implementation is also available (Boltz, 2014).

SRM regions in random colors Different regions from an SRM run starting at $Q=256$ (top left) and going to $Q=1$ (bottom right). Each region is assigned a random color.	SRM regions (average color) Different regions from an SRM run starting at $Q=256$ (top left) and going to $Q=1$ (bottom right). Each region is assigned the average color of that region.

SRM Clustering

In our project, we used Statistical Region Merging for clustering. In contrast to other algorithms, such as k-means, this approach is highly deterministic. For our purposes we only have one SRM run for $Q=256$.

In MATLAB, we use the previously mentioned code from MATLAB Fileexchange (Boltz, 2014). For our Qt-based GUI we implemented the SRM method ourselves.

The SRM clustering reduces the amount of different colors in the image and hence eases the recognition of parts belonging together.

Qlevel = 256;
[maps,images]=singlesrm(double(image),Qlevel);

Segmentation

In the segmentation stage all background regions are removed. This task is quite crucial. If one removes too few, the final stage of finding pathogens might get irritated. On the other hand, if one removes too many regions, positive hits might get removed early before detection. This surely must be avoided.

We opted for a simple thresholding step because it showed that while being easy, it is an effective weapon against the uniform background. In fact, the good image quality we wanted to reach with our device allows now less sophisticated methods. Also the less computational intensive the steps are, the better they might even run directly on the Raspberry Pi in our device!

The HSV thresholding is performed on each component seperately (for more information on the HSV color space we refer to [http://en.wikipedia.org/wiki/HSL_and_HSV Wikipedia]). The first component is the Hue, which we select to be inbetween $0.462$ and $0.520$ to select any blue-greenish (turquoise) color. We will not see bright green due to the filter selection in our device. The saturation value must be high, between $0.99$ and $1.0$. Finally the Value must be between $0.25$ and $0.32$, which assumes a relatively dark-ish color.

Indeed, these values are not problem specific, but specific for each setup and therefore must be determined experimentally.

The remainder of this stage creates a mask of pixels that fulfill the conditions.

% Auto-generated by colorThresholder app on 15-Oct-2014
%-------------------------------------------------------
function [maskedRGBImage] = createMask(srmimg)
RGB = srmimg;

% Convert RGB image to chosen color space
I = rgb2hsv(RGB);

% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.462;
channel1Max = 0.520;

% Define thresholds for channel 2 based on histogram settings
channel2Min = 0.99;
channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings
channel3Min = 0.25;
channel3Max = 0.32;

% Create mask based on chosen histogram thresholds
BW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
    (I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
    (I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);

% Initialize output masked image based on input image.
maskedRGBImage = RGB;

% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;

end

Classification

Automatic Classification

function [mask seg] = automaticseeds(maskedImg)
end

Smoothness Index

For position prediction in virtual environments, jitter or noise in the output signal is not wanted while often present. Since discovering smooth areas is a similar problem to jitter detection, a simple method for determining jitter can be used to measure non-jitter, smoothness [1]. It is assumed that jitter-free areas of a position signal do not differ in velocity.

Smooth areas don't differ in intensity, and therefore only low changes in velocity (intensity change) can be recorded. For the reduction of noise, this operation is performed on the smoothed input image. Then the smoothness $s$ of a pixel $p$ can be determined as: \begin{equation} s(p) = \sum\limits_{p' \in \mathcal{N}_k} \nabla(p') / \arg\max\limits_{p} s(p) \end{equation}

Using a thresholding, $TS_l \leq s(p) \leq TS_u \wedge TI_l \leq I \leq TI_u$, different areas, such as background or pathogen, can be selected.

For the empirical choice of thresholds it can be argued that these are hand tailored to the specific case. While this surely is true to a certain extent, the here presented method has been successfully tested on images from a completely different domain, and no changes to the thresholds have been made to make it work. A proper theoretical evaluation is emphasized, however probably is not the aim of the iGEM competition.

Finally selecting for the red region, this delivers the location of possible pathogens. Since the size of the agar chips is variable but fixed, a quantitative analysis can be performed by counting pixels for instance.

[1] Joppich M, Rausch D, Kuhlen T. Adaptive human motion prediction using multiple model approaches. In: Virtuelle und Erweiterte Realität, 10. Workshop der GI-Fachgruppe VR/AR. Shaker Verlag; 2013. p. 169–80.

Empirical Evaluation

Using our Matlab code we found the lower threshold for the smoothness index to be $TS_l = 0.85$ and the upper threshold $TS_u = \infty$. Similarly for $TI_l = 235$ and $TI_u = \infty$.

For these settings we can find a response already in images taken after 42 minutes.

Ideally one would rate the quality of the image segmentation using some ground truth, such as manual delineations. This yet has to be done for our method. However, from visual observations, our method is showing promising results.

Achievements

• versatile
• robust
• works quickly
• gif show

Source Code

• Matlab Code
• C++ project
• link github

References

[1] Nock R, Nielsen F. Statistical region merging. IEEE Transactions on PAMI. 2004;26:1452–8.

[2] Boltz S. Statistical region merging matlab implementation; 2014. Available from: [http://www.mathworks.com/matlabcentral/fileexchange/25619-image-segmentation-using-statistical-region-merging Matlab FileExchange] . Accessed 12 Dec 2013.