FAST SPOT HYPOTHESIZER FOR 2-DE RESEARCH

Peter Peer and Luis Galo Corzo

CEIT and Tecnun (University of Navarra), Manuel de Lardizabal 15, 20018 San Sebastian, Spain

(P.P. is now with the Faculty of Computer and Information Science, University of Ljubljana

Slovenia, and L.G.C. is with the Asiris Vision Technologies, Astigarraga, Spain)

Keywords:

Image analysis, two-dimensional gel electrophoresis, segmentation.

Abstract:

Two-dimensional gel electrophoresis (2-DE) images show the expression levels of several hundred of proteins

where each protein is represented as a blob shaped spot of grey level values. The spot detection, i.e. segmen-

tation process has to be efﬁcient as it is the ﬁrst step in the gel processing. Such extraction of information is

a very complex task. In this paper we propose a real time spot detector that is basically a morphology based

method with use of seeded region growing as a central paradigm and which relies on the spot correlation

information. The method is tested on gels with human samples in SWISS-2DPAGE (two-dimensional poly-

acrylamide gel electrophoresis) database. The average time to process the image is less than a second, while

the results are very intuitive for human perception and as such they help the user to focus on important parts

of the gel in the subsequent processing. In gels with less than 50 identiﬁed spots as proteins (proteins that

compose a proteome) in the mentioned database, the algorithm detects all obvious spots.

1 INTRODUCTION

Computer vision is a research line which tries to ex-

tract as much information from images as possible.

Biomedical image analysis continues to be an active

area of research, with many encouraging results, but

also with a number of difﬁcult problems still to be ad-

dressed (Duncan and Ayache, 2000).

Two-dimensional gel electrophoresis (2-DE) is

one of the methods able to separate thousands of pro-

teins (Ong and Pandey, 2001). Different cell sam-

ples can exhibit even more than 2,000 proteins. On

such a 2-D gel image, two coordinates character-

ize each protein: its isoelectric point and its mole-

cular weight. Along one dimension, proteins are

sorted electrophoretically according to their pH gra-

dient. They stabilize at points where their net charge

is zero. Along the other dimension, proteins sepa-

rate according to their molecular weight. Thus, the

isoelectric point and the molecular weight uniquely

identify a protein spot in a gel. The separated pro-

teins can be stained with different dyes so that they

are amenable to imaging. The gels are scanned and

normally stored in a database. The process, though

lengthy and subject to enormous experimental un-

certainty, is still much cheaper than other competing

technologies.

The ﬁrst image in Figure 1 (neglect the annotated

crosses) shows a typical image of a 2-D gel. Just by

glancing at it, the reader can imagine how hard a task

it is for any automated algorithm to accurately iden-

tify hundredsof protein spots among the various kinds

of noise, and also to compare and match proteins over

several gels when presented with multiple copies of

gels made from similar cell samples.

There is a critical need for image analysis that

will enable accurate, rapid and reliable spot detec-

tion (Mahon and Dupree, 2001). The spot detection,

i.e. segmentation, process has to be efﬁcient as it is

the ﬁrst step in the gel processing. Namely, inaccu-

rate spot detection has clear ramiﬁcations for the spot

matching process. The segmentation process is par-

ticularly dependent on the staining process (Cutler et

al, 2003). Therefore, a spot detection algorithm with

generic applicability must be capable of effectively

processing a wide range of gels.

In this paper we present a fast spot hypothesizer

for 2-DE research. The user does not have to set any

284

Peer P. and Galo Corzo L. (2007).

FAST SPOT HYPOTHESIZER FOR 2-DE RESEARCH.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IFP/IA, pages 284-289

 SciTePress

parameters in order to segment the image. All the

parameters are automatically evaluated from the input

image itself. The goal of the algorithm is to present to

the user possible spot hypotheses (each spot detection

algorithm actually tries to do exactly this) and in this

way help the user in subsequent gel processing steps.

Before we go to the explanation of our algorithm,

let us ﬁrst take a look at the basic approaches to spot

detection: Edge detection algorithms are traditionally

used in such scenarios (Appel et al, 1997; Lemkin

and Lipkin, 1983). Mathematical morphology based

methods are also widely used (Cutler et al, 2003;

Horgan and Glasbey, 1995; Vincent, 1993). Popu-

lar methods include watersheds by immersion (Vin-

cent and Soille, 1991), marker based watersheds (Vin-

cent, 1993) and H-domes method (Horgan and Glas-

bey, 1995). The scale space blob detection method

can helps us to select the markers (Lindeberg, 1998;

Sporring et al, 1997), which is seldom trivial. Our al-

gorithm is basically a morphology based method with

use of seeded region growing as a central paradigm,

which is a version of a watershed technique (Russ,

1995).

2 MATERIALS AND METHODS

2.1 Algorithm

In a recognition system a preprocessing step to seg-

ment the pattern of interest from the background,

noise etc. usually precedes (Jain et al, 2000) the ac-

tual recognition process and for the current task this

is no exception. 2-DE images show the expression

levels of several hundred of proteins where each pro-

tein is represented as a blob shaped spot of grey level

values. The segmentation task at hand consists of a

separation of the image into what is background and

what are spots and the challenging part are the cases

of overlapping spots, varying background and a high

level of noise in the images.

Namely, gel images are normally very noisy, so

the ﬁrst step in the algorithm is to reduce the inﬂu-

ence of noise on the subsequent processing. The in-

put image is thus ﬁrst processed with a Median ﬁlter

(Russ, 1995). The 3×3 window that is used inside

its implementation successfully removes salt and pep-

per noise (Russ, 1995) and only softly smooths the

image, while preserving edge information. In other

words, it eliminates isolated pixels and gently blurs

spot shapes, but enhances edges. Filtering is one way

to address the problem of noise. The second one is

by reducing the image size. By testing our algorithm

on our developmental set of images (different image

size, different dye etc. as in the testing set, which con-

sist of gels with human samples in SWISS-2DPAGE

(2-D polyacrylamide gel electrophoresis) database;

http://www.expasy.org/ch2d/ (Hoogland et al, 2000)),

we noticed that processing of ﬁrst ﬁltered and then

downsized images gave the best results. Therefore,

after ﬁltering, the input image is downsized to the

approximate width of 500 pixels (with maintained

aspect ratio). In this way we smooth the spots a

bit more, eliminate some remaining Gaussian noise

(Russ, 1995) and also speed-up next steps of the algo-

rithm. In the process of noise reduction we conform

to the rule that in any ﬁtting or smoothing operation

the window size has to be smaller than the features of

interest (Russ, 1995). Thus, in this step we reduce the

noise and end up with more compact representation

of spots.

The next step is to dynamically identify the back-

ground. This is achieved by applying a two-step Otsu

thresholding technique (Otsu, 1979). The input to

Otsu thresholding technique is a histogram of the in-

put image, which is then divided in two classes and

the inter-class variance is minimized. Since a num-

ber of spots in the gel image are weakly expressed,

we soften the border between the two classes, namely,

spots and background, by applying Otsu technique in

two steps. First we calculate the basic threshold and

then this value is used to calculate the new, soften

threshold based only on pixels in the image that are

lighter then the calculated threshold. This dynami-

cally obtained global threshold is then used to elimi-

nate the background. For more details about the tech-

nique is (Otsu, 1979).

To identify spot hypotheses, we interpret the in-

tensity as the third dimension information in the in-

put image. We employ another operator in the 3×3

window size to identify local peaks. The peak is es-

tablished if the pixel in the middle has the same or

darker value as all surrounding, neighboring pixels.

Generally, this operator is called 8-neighborhood ﬁl-

ter (Russ, 1995). Giving the pixel the possibility to be

of the same value as the neighboring pixel has two

advantages. First, saturated spot peaks, i.e. spots

with ﬂattened peaks, are detected. The second one ad-

dresses a common problem in the spot detection – a so

called shoulder problem. A shoulder in the context of

spot detection can be described as two merged spots

with one peak higher then the other and no lighter

pixel values in comparison to the small peak between

the peaks in question. In our case, if there are at least

two pixels of the same intensity in this small peak,

this so called shoulder, we detect it and treat it as a

possible spot. Note that this deﬁnition of a shoul-

der is a simpliﬁcation of a generic deﬁnition, while

one spot can also be integrated inside the other spot.

Such problems are normally addressed by paramet-

ric spot modeling with Gaussian, diffusion or mixture

spot model (Bettens, 1996; Rogers et al, 2003).

Now that we have the information about peaks, we

can correlate them in order to investigate spot sizes.

But ﬁrst we have to ﬁnd the center of mass of each

peak as they could be saturated, i.e. a region big-

ger than one pixel can be labeled as peak. Normally,

each spot is, among other information, represented

by its x and y coordinate of the peak (Taylor et al,

2003). In order to do this, we employ seeded region

growing, a version of a watershed technique (Russ,

1995). A seed can be the ﬁrst pixel in the peak and

we recursively visit all the pixels in the peak region.

In this way we calculate for each peak its center of

mass. While visiting all the pixels in the region, we

also gather other information, i.e. moments, like the

size of the bounding box and the number of pixels in-

side the region. This information will be used after

the spot detection to make the hypotheses about spots

and their relations on the higher level. We get back to

this in the continuation of the paper. For more details

about the seeded region growing method see (Russ,

1995).

The ﬁrst step towards establishing correlation of

spots is to ﬁnd the nearest neighbor for each iden-

tiﬁed peak. For this task Euclidean distance (Russ,

1995) seems the most logical choice. For each near-

est neighbor we also calculate its direction, which is

again something that can be used in the subsequent

processing.

The basic correlation information suggests the re-

gion for each peak in which the whole spot should

lie. The distance between the peak in question and

its nearest neighbor determines the radius around the

peak in which the spot should appear. Inside of each

of such regions we basically do the following: we use

two-step Otsu technique locally in the region (on the

image from which we eliminated noise) to determine

the local sub-region of the spot and then grow the spot

inside obtained sub-region from the peak with a shape

constraint (the details are given in the continuation).

To determine the sub-region of the spot, we in

this case employ only the second step of our two-step

Otsu technique, as the peak value is used instead of

the threshold that we would obtain in the ﬁrst step.

In this way we eliminate the inﬂuence of the nearest

neighbor that is darker than the peak. After the lo-

cal threshold is obtained, only the pixels with darker

intensity than the threshold and pixels with lighter or

equal intensity than the peak in the region are kept.

Thus, we deﬁne a sub-region, which should contain

the spot. For subsequent processing, i.e. for steps af-

ter spot detection, after establishing spot hypotheses,

we can collect similar information about this region

as for the peak region, plus other moments, like the

power of the region, its orientation and semi-major

axis (Russ, 1995).

One of the commonly used descriptors of the spot

suggested in PEDRo (Proteomics Experiment Data

Repository) model (Taylor et al, 2003) is also spot

radius. Fact is also that the radius is the ideal 2D de-

scriptor for the ideal spot and it enables better visu-

alization and interpretation for humans. In the light

of this, we grow the spot from the peak in the calcu-

lated sub-region with a circular constraint. Thus, we

again use seeded region growing, a version of water-

shed technique, but now the implementation is done

in the iterative manner, each time adding a circle of

pixels around the peak until all the pixels are in the

deﬁned sub-region (Russ, 1995).

The pseudo-code for the steps in the proposed al-

gorithm are as follows:

begin

RemoveNoise();

FindBackground();

FindPeakRegions();

CalculatePeakMoments();

FindNearestNeighbors();

for each peak in its neighborhood do

FindLocalBackground();

GrowSpot();

end

When all the peaks are processed in this way, we

end up with the segmented image and a linked list of

information about each spot. For subsequent process-

ing we add one more information: the difference be-

tween the peak intensity and the average intensity of

pixels on the border of the circular description of the

spot. This gives us a perspective on the height of the

spot and could be a valuable information for instance

in the elimination of non-spots. The segmented im-

age can now be superimposed over the original im-

age with different degree of blending in order to help

the user to focus on important parts of the gel for its

subsequent processing. Blending can be implemented

with a slider that blends the images based on the po-

sition of the handle on the slider, where each extreme

of the slider represents one image, original and seg-

mented. In this way the segmentation results become

even more intuitive for human perception (see Figure

1).

2.2 Evaluation Methodology

In order to build a system that can succeed in a realis-

tic environment, certain simpliﬁcations and assump-

tions about the environment and the problem domain

are generally made. The use of a priori information is

critical. Thus, the algorithm was ﬁrst sketched based

on the gel properties. Then designed, with no internal

parameter tuning as the ability of the system to dy-

namically adapt to the changing environment is also

important, and then tested! At the design time only

two small crops from one differential proteomics ex-

periment were used to visually contrast the results

to our expectations. Our expectations were actually

a ground truth manually marked spots to which we

contrasted the results of each step of the algorithm.

This was done in order to prove the correctness of our

sketch of the algorithm. The crops were from the gels

in which a mouse liver sample was run with comassie

blue staining.

Different laboratories have somewhat different

opinions about different dyes. What we normally do

is use ﬂuorescent dye for visualization. We see co-

massie blue as a very useful dye for spectrometry

analysis but worse for image analysis, because some-

times there are discrepancies to select a correct spot.

On the other hand, the normal process in SWISS-

2DPAGE database, on which our algorithm is tested,

is to produce a silver stained gel as the reference.

They use Melanie software (Appel et al, 1997) to

detect spots and before counting the spots they also

manually edit them with the software. Note that this

is quite a normal procedure after the spot detection to

eliminate/add certain spots for subsequent processing

of gels. Then successive experiments provide some

spot identiﬁcations after spot cutting. In their case,

protein visualization is generally with comassie blue

dye. That is why some spots may have been iden-

tiﬁed and seen in the comassie blue stained gel, but

not visible in the reference silver stained gel. This re-

ﬂects in the fact that some crosses that mark identiﬁed

proteins in the database are superimposed on the im-

age where there is ’nothing’ beneath them in the ref-

erence gel. The matching between both images (silver

versus comassie) is again done with Melanie software

after manual correction, thus positions should be al-

most ‘exact’.

Unfortunately, we cannot simply count true posi-

tives (real spots), false positives etc., since the ground

truth information is not available. Moreover, when it

comes to the human factor such information is very

subjective and varies even if the same person tries to

provide this information at different occasions (e.g.

try to mark the same image after one month and com-

pare the markings).

Based on these facts we shaped our expectations,

criteria and wishes for the evaluation of the algo-

rithm: To be as objective as possible, we contrasted

our results with the results published about the

content of SWISS-2DPAGE database. Namely,

we compared our results, obtained with applying

our method to the gels with human samples in the

database, with the reported number of detected

spots and the number of identiﬁed spots in the gels

with less than 50 identiﬁed spots (Hoogland et al,

2000) (http://www.expasy.org/ch2d/relnotes.html).

We knew that our algorithm should be very fast

on nowadays standard personal computer; that we

don’t manually edit the results, while they did; that a

different dye will be used as in the design examples;

that some crosses that mark identiﬁed proteins in the

database are superimposed on the image where there

is ’nothing’ beneath them in the reference gel.

In the light of this, we expected that the number of

detected spots would vary from the published num-

bers, even drastically in some cases. But we hoped

that the difference will be as small as possible, that

we will detect more spots in average, revealing more

possible spot hypotheses and, most importantly, that

we will detect all obvious identiﬁed spots as this in-

formation is the best ground truth available, giving the

real proteins, the proteome.

3 RESULTS AND DISCUSSION

The average speed of processing a gel in the data-

base was really high. It took only 0.855 of a second

per gel on a single processor personal computer (In-

tel Pentium IV 3.0GHz). The tests were performed

in MS Visual Studio C++ Debug mode, which is ap-

proximately 2.3 times slower than Release mode. In

SWISS-2DPAGE database there are 17 2-DE images

with quite a big pH range: from approximately 3.5–

10 on the big end to 3.9–7.5 on the small end. Most of

them have the range of the former. This informationis

very important since with bigger range we get lower

resolution of proteins and consequently the detection

of spots is harder.

Table 1 gives the details about the algorithm per-

formance on each gel. The abbreviations used in the

table are the same as in SWISS-2DPAGE database:

CEC, colorectal epithelia cells; CSF, cerebrospinal

ﬂuid; DLD1, colorectal adenocarcinoma cell line;

ELC, erythroleukemia cell line; HEPG2, hepatoblas-

toma carcinoma derived cells; HEPG2SP, hepatoblas-

toma carcinoma derived cell line secreted proteins;

HL60, promyelocytic leukemia derived cells; RBC,

red blood cells; U937, macrophage like cell line. In

11 out of 17 cases we detected more spots as reported

on SWISS-2DPAGE web portal and also in (Hoog-

land et al, 2000). This is exactly as we expected and

Table 1: Evaluation of the proposed method on the gels

with human samples in SWISS-2DPAGE database: N gives

the number of detected spots with our method and M − N

gives its difference with the number of detected and, be-

fore counting, manually edited spots with Melanie software.

M is published on SWISS-2DPAGE web portal and also in

(Hoogland et al, 2000).

Gel N M-N (M-N)/N

[%]

CEC 3136 -62 -2

CSF 2220 -556 -25

DLD1 2898 542 18.7

ELC 2709 -565 -20.9

HEPG2 2242 620 27.7

HEPG2SP 1686 48 2.8

HL60 2718 446 16.4

KIDNEY 2914 -18 -0.6

LIVER 2427 -14 -0.6

LYMPHOCYTE 1175 -249 -21.2

LYMPHOMA 2013 -123 -6.1

NUCLEI LIVER 3584 -2587 -72.2

NUCLEOLI H. 2826 -1555 -55

PLASMA 1694 -272 -16.1

PLATELET 1931 262 13.6

RBC 1307 493 37.7

U937 1998 -1103 -55.2

hoped for as we are presenting spot hypotheses in or-

der to help the user to focus on important parts of the

gel. Only in 4 cases this number was much bigger

than expected; the difference between the number of

detected spots with our method and the reported num-

ber of detected and, before counting, manually edited

spots with Melanie software was more than 30% of

the number of detected spots with our method (see

the fourth column in Table 1). On the other hand, in 4

cases the number was almost identical to the reported

one; the difference was less than 3% of the number

of detected spots. Note also that since our results are

contrasted to the results obtained with Melanie soft-

ware after manual editing, the latter results about the

number of spots are less objective than we would like.

In general, we were quite satisﬁed with the ob-

tained results but, based on presented observations,

still not completely convinced in the method efﬁ-

ciency. Thus, we looked at another reported infor-

mation, which should be more objective: the number

of identiﬁed spots. These are identiﬁed proteins that

compose a proteome. We looked at the gels with the

number of identiﬁed spots less than 50 and made the

following hypothesis: If the algorithm can detect all

obvious identiﬁed spots, especially in the cases where

the number of detected spots is smaller than the re-

ported one, then we can say that the algorithm is ef-

ﬁcient in revealing spot hypotheses. What is an ob-

vious spot for a human observer? An obvious spot is

a spot that does not have a property of a shoulder, as

described before, or that it is not missing in a given

gel, because of the procedure how the spot was ob-

tained and then projected into the reference gel, also

as described before.

Table 2: Evaluation of the proposed method on the gels

with human samples in SWISS-2DPAGE database, where

the number of reported identiﬁed spots is less than 50: P

gives the number of detected spots with our method that

overlap with marked spots in the database and R − P gives

its difference with the number of reported identiﬁed spots.

R is published on SWISS-2DPAGE web portal and also in

(Hoogland et al, 2000).

Gel P R-P Reason

ELC 34 1 shoulder

HL60 26 0 /

KIDNEY 43 1 not visible

PLATELET 40 1 shoulder

U937 41 1 shoulder

The results are presented in Table 2. From them

we can see that with our method we obtain all ob-

vious identiﬁed spots, even in the case of HL60 and

PLATELET, where we detected many spots less than

reported by SWISS-2DPAGE project. Based on these

results and our hypothesis expressed in the previous

paragraph, we can now conclude that our algorithm is

efﬁcient in revealing spot hypotheses.

In contrast to the numerical, quantitative results

presented in Tables 1 and 2, Figure 1 gives visual,

qualitative insight into the method’s efﬁciency. For

better visualization the segmented image is superim-

posed over the original and annotated image with dif-

ferent degree of blending, as described before. The

ﬁgure shows a crop from the LIVER gel, where the

identiﬁed spots, i.e. spots corresponding to known

proteins, are marked with crosses.

4 CONCLUDING REMARKS

The paper presents a novel algorithm, a sequence of

steps, which leads to the spot segmentation of 2-DE

images. We cannot expect that it will be equally ef-

ﬁcient in all possible cases, on all possible gels, but

it gives us a good starting point for the subsequent

processing. The fact is that its results, the spot hy-

potheses, are very intuitive for human perception and

Figure 1: Visual example of results; a crop from the LIVER

gel: the segmented image is superimposed over the orig-

inal and annotated image with different degree of blend-

ing: from the original image to 22%. blended image, 46%

blended image and the segmented image.

us such they help the user to focus on important parts

of the gel.

A lot of work is still in front of us: the use of col-

lected information for automatic elimination of spot

hypotheses, for addressing over-segmentation prob-

lems and for establishing the hypotheses for trains of

spots, the use of other shape constraints to redeﬁne

the spot boundaries, addressing the shoulder prob-

lem, identiﬁcation and regrowing of overlapped spots

(to their actual size) and/or manual editing or interac-

tive reﬁnement (adding, deleting, merging, splitting)

of results etc.

ACKNOWLEDGEMENTS

Special thanks go to Victor Segura, Enrique Santa-

mar´ıa and Fernando J. Corrales for valuable discus-

sions about the 2-DE, the spot detector and its appli-

cation. We would also like to express our gratitude to

Christine Hoogland for valuable discussions about the

ExPASy (expert protein analysis system) proteomics

server, SWISS-2DPAGE and Melanie software.

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