Efficient Semiautomatic Segmentation of Plant BiologicalObjects Based on Live-wire Methods

Wolfram SchoorFraunhofer Institute forFactory Operation and

Automation (IFF)Sandtorstrasse 22,

39106 Magdeburg, Germanywolfram.schoor@iff.fraunhofer.de

Thomas SeidlFraunhofer Institute forFactory Operation and

Automation (IFF)Sandtorstrasse 22,

39106 Magdeburg, Germanythomas.seidl@iff.fraunhofer.de

Felix BollenbeckFraunhofer Institute forFactory Operation and

Automation (IFF)Sandtorstrasse 22,

39106 Magdeburg, Germanyfelix.bollenbeck@iff.fraunhofer.de

Udo SeiffertFraunhofer Institute forFactory Operation and

Automation (IFF)Sandtorstrasse 22,

39106 Magdeburg, Germanyudo.seiffert@iff.fraunhofer.de

Bernhard PreimOtto von Guericke University,

Universitätsplatz 2,39106 Magdeburg, Germany

preim@isg.cs.uni-magdeburg.de

Rüdiger MeckeFraunhofer Institute forFactory Operation and

Automation (IFF)Sandtorstrasse 22,

39106 Magdeburg, Germanyruediger.mecke@iff.fraunhofer.de

ABSTRACT

This paper presents a novel method for efficient semiautomatic multi-label segmentation of plant biological image data. Theapproach extends live-wire methods in order to facilitate exact user-steered segmentations for atlas generation. By integratinga segmentation-specific user interaction model into the live-wire formulation i) more exact segmentations, ii) increased com-putational efficiency, iii) without loss of generality are achieved. The concept of mutual influence of image feature based pathcosts and user input uncertainty are consistently combined. By incorporating user behavior into cost based delineation a moreintuitive user interface is obtained also yielding in a more accurate segmentation. We introduce path-based methodologies,specific user interaction models and propose the combination of both of them. The purposefulness of the method is shown inan application comprising segmentation of histological section data supporting the generation of 3-D atlases.

Keywords: computer vision, user interaction, live-wire.

1 INTRODUCTIONImage segmentation is the basic principle behind vari-ous image processing tasks. The main goal is to obtaina higher level of information than a purely numericalrepresentation of an image. The pixels are thereforecombined into semantically equivalent groups [Tön05].Segmentation consists of two steps, recognizing rele-vant objects in an image and delineating exact spatialdimensions and their features [FUM00]. Different al-gorithms that can automatically segment plant biologi-cal image data have been introduced for this non-trivialtask [BS08] or [DBS+08]. These algorithms are gener-ally based on existing reference data. Such model datais not available to generate atlases of unexplored plantbiological histological images and must therefore eitherbe segmented manually or semiautomatically. A setof semiautomatically segmented image slices (approxi-

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WSCG 2009 communication paper proceedingsWSCG’2009, February 2 – February 5, 2009Plzen, Czech Republic.Copyright UNION Agency – Science Press

mately 5% of a serial section image stack) can replacethe purely manual segmentation of plant biological im-age data as presented in [GDB+07]. Image data con-sists of previously sectioned plant biological objects,i.e. caryopses of the Hordeum Vulgare, digitized witha color CCD camera with a resolution of 1600 x 1200pixels under a light microscope. To facilitate their pro-cessing, the digitized images were converted to an 8 bitgray scale representation without any significant loss ofrelevant information (see [SBH+08]). Figure 1 is animage for such a segmentation task. The detail of thesegmented region bounded in blue reveals obvious dif-ferences in the contour’s characteristics (red and greenbox).

Figure 1: Slice of Hordeum Vulgare with different tis-sue characteristics (red and green box).

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2 RELATED WORKA graph-based semiautomatic interactive segmenta-tion method, namely live-wire, was introduced byMORTENSEN et al. [MMBU92], [MB95], [BM97]and is related to geometric active contours presentedby KASS et al. [KWT87]. The key feature of live-wire methods is their exact extraction of a boundarysegment between a start point Ps and an end point Peindicated by the position of the input device. The taskof segmenting an entire region is broken down intooptimal delineation of several parts comprising theentire contour. To do so, a cost map is calculated basedon each pixel edge’s properties and interpretation. Agraph is introduced reflecting the cost map, whichallows a path search for optimal path computation bythe minimal accumulated path costs (cf. [HNR68]A*-algorithm). A set of weighted parameters derivedfrom the pixel’s neighborhood is employed to computethe path between the start and the end point. In anideal case, a rough sketch of the contour suffices toextract the segment boundaries. Figure 2 illustratesthe principle of the live-wire method. For clarity’ssake, only a 4-cell neighborhood was selected for thevisualization.

(a)

(b)

Figure 2: (a) portion of a gray scale image, (b) showsrespectively the graph, resulting from intensity differ-ences. The intensity values appear in the nodes, relatedcost components between the nodes. Blue points indi-cate the optimal connection along the lowest total costcomponents (dashed line). The path follows the dark

pixels from the upper left to the lower right corner.

3 METHODSA core element of the live-wire method is the calcula-tion of the aforementioned costs related to the edges inthe graph. Depending on the image data, different costfactors can be applied for good segmentation results.The following listed cost factors constitute a combi-nation of different cost factors, which are suitable forthe used image data. The cost factors are derived from[MMBU92], [MB95] and are also based on own formu-lations.The impact of different cost factors is weighted as anoverall cost function. Let p and q be two adjacent pix-els in an image slice S. The costs for a connection fromp to q can be determined by a cost function C(p,q), ren-dered thusly:

C(p,q) =N

∑n=0

ωnCn(p,q), (1)

where N denotes the count of the different cost fac-tors, ωn the respective weighting factors and Cn(p,q)the value of the n-th cost component between p and q.The cost factors Cn can be differentiated as static anddynamic factors:Static factors are calculated before segmentation andare independent of user interaction. Static cost factorsindicate the cost to directly connect one pixel to its mneighbors in the graph-based path search. Filter opera-tions are applied to determine the static cost factors (seeTable 1):

Cost Factor Cost Identifier (Cn)Laplacian CL

Laplacian of Gaussian CLoGGradient magnitude CGM

Roberts Cross CRXDifference of Intensity Values C∆I

Brightness Feature CBF

Table 1: Components of local cost functions betweenadjacent pixels.

An individual local cost factor is calculated followingEquations 2-10:

CL(p) = 1− (Lkernel(x,y) ·S) (2)

Lkernel =∂ 2 p∂x2 +

∂ 2 p∂y2 (3)

were each summand in Lkernel stands for the second par-tial derivation in x respectively in y direction.

CLoG(p) = 1− (LoGkernel(x,y) ·S) (4)

LoGkernel =− 1πσ4

(1− x2 + y2

2σ2

)e−((x2+y2)/2σ2) (5)

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CRX (p) = 1− (RXkernel(x,y) ·S) (6)

RXkernel =∂ p∂x

+∂ p∂y

(7)

CGM(q) = 1− 1max(G)

√(dS(x,y)

dx

)2

+(

dS(x,y)dy

)2

,

(8)max(G) represents the largest gradient magnitude in theimage. The C∆I(p) cost can be calculated as follows:

C∆I(p) =|I(p)− I(q)|

2B −1, (9)

where I(p) and I(q) denote the intensity value of theirrespective pixel positions and B the color depth of thecurrent image. The brightness cost function CBF can beexpressed as:

CBF(p) = I/max(I), (10)

where low intensity values I correspond to low costsCBF .The dynamic costs cannot be computed in advance ofa path search. They are computed during segmenta-tion and reliant on user input. Table 2 lists the dynamiccosts.

Cost Component Cost Identifier (Cn)L0 distance to direct path CL0L1 distance to end point C∆P

Table 2: Dynamic components of the cost function.

Equation 11 calculates the orthonormal vector ~v to thestraight line between Ps and Pe.

~v =

(Pey −Psy

−(Pex −Psx)

)√

(Pex −Psx)2 +(Pey −Psy)2(11)

Equation 12 determines the vector~r from the start pointPs to the present location q.

~r =(

Pqx−sx

Pqy−sy

)(12)

Projecting~r onto the normalized vector ~v yields the L0distance from point q to the straight line spanned by Psand Pe and called dL0(q) (see Equation 13).

dL0(q) =~v ·~r = vx rx + vy ry (13)

The costs increase as the pixel’s distance to the idealconnection path increases (see Equation 14).

CL0(Ps,Pe,q) = g(dL0(q)) (14)

The function g(x) may express this influence in a vari-ety of ways, e.g. as a linear dependency or square root.Equation 15 provides an appropriate method:

g(x) = a x2 (15)

This quadratic function penalizes small deviationsfrom the ideal line with small cost values, whereaslarger distances become comparatively expensive. Theincreasing coefficient a of 0.25 damps too high costvalues for distant input device positions.

The cost function C∆P represents the costs of a pixel qemerging from the distance to the end point Pe in re-lation to the neighboring pixel’s p distance to Pe (seeEquation 17). The term d∆P(i) with i ∈ {p, q} de-notes the L1 distance (Manhattan distance) to Pe and iscalculated with Equation 16.

d∆P(i) = (Pex − ix)+(Pey − iy) (16)

C∆P =

0.0, if d∆P(p)−d∆P(q)≥ 10.5, if d∆P(p)−d∆P(q) = 01.0, if d∆P(p)−d∆P(q)≤−1

(17)

The basic live-wire methods may be extended in avariety of ways, e.g. by extending the search space tothe third dimension [SPP00], [SPP01], [SUG+06] orby extracting the segmentation parameters for segmentsor segment parts [FUS+98] or [EKS92].A detailed discussion of these extension is out ofthe scope of this paper. For a detailed analysis oflive-wire’s implementation and interaction methods forimage segmentation, the authors refer to [BM97] or[OS01].

4 EXTENDING THE LIVE-WIREMETHOD

In [SBH+08], SCHOOR et al. introduced interaction-based support of segmentation, i.e. speed-dependentautomatic zooming (SDAZ) based on IGARASHI[IH00] and pseudohaptic feedback (see LÉCUYER[LBE04]), to improve the segmentation process interms of segmentation speed and accuracy of results.A user study demonstrated that this also works inpractical use [SBH+08].Different application-dependent algorithms and heuris-tics accelerate the run- and reaction-time of theapplication presented here.

4.1 Restricting the Search-SpaceApart from the large quantity of image data, the maindisadvantage is that only a fraction of the data isrelevant to the determination of the correct path. A

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combination of new and established strategies com-putes paths faster and supports real-time interaction.

Decreasing the Global Search-Space:In this very important step, the information space mustbe narrowed before calculating the path, even beforegenerating the cost map. This can be done by simplythresholding the image and ignoring undesired intensityvalues in image parts or pixels.Due to the nature of the image data approximately 50%of the pixel amount can so be masked in advance of allfurther computational steps. Unimportant areas (black)can be masked as in Figure 3.

Figure 3: Original (top) and masked (bottom) imageslice.

On the Fly Path Restriction:The segmentation of object boundaries can be difficult,especially if the boundaries are discontinuous andvery close to each other (see Figure 1). Manually orautomatically setting a user-defined control point inthe system restricts the search space to a rectangularpath search between the two consecutive points and apredefined pixel distance to the direct path connection.Unlike [SPP00] where the target search space canbroadly be masked before segmentation starts, thismethod restricts the path on the fly between the lastsegment and the new current point. Figure 4 presentsthe principle. The yellow border encloses the searchspace with the present distance parameter.This restriction is useful because a pixel’s corre-sponding node is not expanded in the graph if itsdistance to the ideal line exceeds the defined value.This limits the depth of a path search and allows itto be terminated faster. The expected speed increasewill be small in regions with strong straight edgesbut will prevent the bleeding of the contour in ho-mogeneous regions with weak edges. Selecting asmall distance value accelerates a path search (verysmall information search space) but attracts the con-

Figure 4: A border (with predefined pixel distance tothe ideal line between two consecutive points) is used torestrict the information search space for the path search.

tour line to the ideal line between the two consideredpoints and can therefore differ from the required values.

Speed Dependent Path Restriction:The speed of user interaction during segmentation is acharacteristic feature of segmentation (see [SBH+08])that can be used to determine whether homogeneousregions or curvy contours must be segmented. Thisallows determining the distance value automatically.If user interaction during segmentation is slow, thedistance value should be decreased.

Masking of RegionsRecalling LUCCHESE [LM01], that the segmentation isa subdivision of the image into n disjunct sections, a se-quentially performed segmentation of two adjacent re-gions can cause the formation of “isles”, due to filteroperations, user inaccuracies or other reasons. To avoidthis and speed up the segmentation process a maskingof already segmented regions ensures an ideal segmen-tation of the image (one pixel only belongs to one re-gion). If a user navigates into one masked region, thepixel adjacent to that regions’s boundary is selected asthe current point for path extraction. Figure 5 presentsan example of a new path segment (blue) adapting tothe region masked in red.

Figure 5: The masked region (transparent red) cannotbe segmented again.

Tiling of the Image:The mass of histological image data make it impossible

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to guarantee that the different subsequent image basedfilter operations are calculated efficiently, especiallywhen other time-critical calculations must be solvedsynchronously. One solution is to use graphics hard-ware as numerical processor as outlined in [RVC07].The drawback of this solution is the need for appropri-ate graphics hardware in the meaning of limited texturesize. Another solution is to break down an image seg-mentation problem into smaller elements, e.g. by us-ing overlapping tiles as demonstrated in CRISP et al.[CPR03]. The overlap of the tiles depends on the max-imum filter size used. An example is presented in Fig-ure 6.The graph segment corresponding to a tile is generatedwhen:

1. The input device enters a new tile

2. The path search arrives a new tile

3. Enough time for further tile computation is left(concerning the real time interaction criterion)

Figure 6: Tiling a large image slice with overlap.

4.2 Improving User InteractionThe system’s real time response speed during segmen-tation can be assumed. More important is a simpleinteraction mechanism that supports users during thetime consuming task of segmentation.

Automatic End Point Shifting:The contour of the region between the last point andthe input device’s current position is calculated duringsemiautomatic segmentation. Control points and inputdevice positions are entirely bound to user interaction.The connection between these points can be calculatedvery efficiently. Control point position and end pointposition must therefore be placed exactly. Manuallysetting control points can be time consuming andentails additional user effort. A possible end point isset orthogonally to the line between the last controlposition and the input device’s current position (seeFigure 7).

Automatic and Manual Control Points:The complexity of path calculation depends directly

Figure 7: Scheme to calculate potential end points.

on the path length. Are start and end points for thepath calculation further away from each other, morepixel have to be taken into account for the correctpath determination. A divide and conquer strategy isemployed to accelerate path determination. The userroughly sketches the contour to segment. If the inputdevice position exceeds a predefined distance ∆d to thestart position or a predefined time interval ∆t , a controlpoint is automatically added to the position. The pathdetermined becomes fixed.The path determination for the whole path will becalculated in the following only between the lastcontrol point and the end point respectively the nextautomatically or manually inserted control point.Figure 8 presents a segment part (red dashed line)following a segment boundary. Green points arecontrol points manually inserted in difficult positionsin the image (e.g. intersecting lines, line breaks, sharpchanges of direction, etc.). Purple points are cooledcontrol points that have remained unchanged for along time (see [BM96] for cooled control points) orhave a distinct number of following control points andtherefore, cannot be deleted by incidence (for exampleby doing a segmentation loop). Blue points are recentlyinserted control points. These points can be deleted byentering a point surrounding region with a predefinedradius. Purple points can then warm up again, i.e. turnblue again.

Figure 8: Temporary segment part with manually in-serted control points (green), automatically insertedbreak points (blue) and locked break points (purple),

also shown in closer detail.

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Disabling the Path Search:Simply deactivating a path search may be advantageousin some cases, e.g. if the contour line changes radicallyover time. The parameters initially used to extract thecontour may result in erroneous segmentation results.Thus, the segmentation can be partially done manuallyby contemporaneously pressing a predefined key.

5 RESULTSThe algorithms presented were tested on histolog-ical plant biological data sets. The results fromthe segmentation of a representative number of im-age slices can be used for the method of automaticsegmentation proposed by BOLLENBECK et al. [BS08].

5.1 ExperienceIn Figure 9 a complete segmentation of a histologicalslice is shown with respective tissues.

Figure 9: Labeled histologic image slice with legendof tissues.

Experimental studies evaluated the weights of theparticular cost factors. The cost factors are mapped inthe normalized interval [0, 1]. This enables a uniformweighting due to the weighting factors. Table 3presents the different tissues of a grain caryopsis andtheir weighting factors. The weighting factors arebounded by the interval [0, 100]. These experimentalevaluated values can be used for similar segmentationtasks of histological plant biological image data asinitial values for the used cost factors. This can leadto a faster setting of the cost factors for well suitedsegmentation results.Figure 10 presents a part of a reconstructed model andthe respective sample segmentation slices.

5.2 User TestsThe developed live-wire method was used in practicaltests for the segmentation of biological plant data.Users were asked to segment the endosperm of thebarley grain, which is shown in Figure 9 (violet). Thissegment was chosen because its border is defined bydifferent features, including strong clear edges but also

Tissue CL CLoG CGM CBF CRX C∆I CL0 C∆P

Exterior 30 20 0 20 15 25 5 5

Vascularbundle

10 20 5 15 20 10 10 10

Pericarp 30 20 0 20 15 25 5 5

Transfercells

20 15 10 20 15 20 5 5

Blowhole 25 25 5 10 10 20 5 5

Nucellarprojection

30 25 0 20 10 10 5 5

Endosperm 30 25 0 20 15 10 10 5

Table 3: Weights of the different cost factors for eachtissue.

Figure 10: Reconstructed upper model part of a grainmodel using sample segmentations

weak unclear or even interrupted edges.The users were also asked to segment the chosenpart completely manually without system assistance.Eight out of ten users achieved faster results withthe here proposed semiautomatic method. Partiallythe live-wire method was twice as fast as the manualsegmentation. The two users, who needed more timedeclared that they did not use the option to switch tomanually segmentation during the process.This leads to time-consuming segmentation attemptsby repeating wrong segmentations in some parts ofthe image with weak edges or large gaps betweencorresponding edges.

Accuracy:According to MORTENSEN AND BARRETT [MB95] theaccuracies of the users segmentations were comparedwith a segmentation, which was manually done by anexpert (gold standard). The resulting accuracy graphsshow a very high degree of similarity over all segmen-

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tation tools which were tested. The commercial soft-ware solutions achieve a better correlation than the hereprovided software in the range of very small devia-tions to the gold standard (especially Photoshop). Ifthe Hausdorff-distance is greater than 2 pixels the pre-sented approach performs best.With respect to physical diameters of a tissue boundary,which can be for example up to 4 pixels in size in theimage data, Hausdorff-distances of 3 or 4 pixels are stillwithin an acceptable accuracy range.

Accuracy

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7

Hausdorff-distance

Percentage

TestApp

Photoshop

MeVisLab

amira

Figure 11: Accuracy results in comparison with othersegmentation tools.

Reproducibility:By comparing the results of different segmentationswith each other the developed method had a high repro-ducibility in intra-user and inter-user comparison (seeFigure 12 a) and Figure 12 b)). That means that differ-ent segmentations of one user were almost the same butalso the segmentations of different user had a very highsimilarity.

Usability:Asking the users for a subjective rating of the presentedmethod, nine out of ten users confirmed a good usabil-ity of the presented method, which is easy and fast tolearn. Especially the automatic placement and the dif-ferent states of the control-points were appreciated bymost users.

6 CONCLUSIONSA variety of enhancements improve semiautomaticsegmentation tasks on digitized plant biological serialsections. A user test demonstrated that the systemis suitably usable. The objective improvements incomparison to commercial segmentation software mustbe further evaluated. The presented system has beenproven to be as accurate as amira www.amira.com,MeVisLab www.mevislab.de and Photo-shop’s www.adobe.com semiautomatic segmen-tation capabilities for selected image data.The system was especially designed for large image

Intra-user Reproducibility

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5

Hausdorff-distance

Percentage

T1/T2

T1/T3

T2/T3

a)

Inter-user Reproducibility

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5

Hausdorff-distance

Percentage U1/U2

U1/U3

U1/U4

U2/U3

U2/U4

U3/U4

b)Figure 12: The reproducibility is shown on the basis ofone histological image slice segmentation. The match-ing results of three segmentations T 1−T 3 of one userare shown in a) and the matching of segmentations offour different users U1−U4 in b). The X-Axis standsfor the difference of the compared segmentation resultsin pixel (Hausdorff-distance). The ordinate indicates

the amount of pixel which fulfill this criterion.

slices. In contrast, standard medical applications (e.g.CT/NMRi) use typically less than a quarter of the hererequired image size for segmentation tasks.The combination of mutual influence of image featurebased path costs and user input uncertainty lead tofaster and more accurate segmentations than manualsegmentations. The incorporation of user behavior intoa cost based delineation was perceived as intuitive byalmost all users.Furthermore, path-based methodologies and specificuser interaction models were introduced and the use-fulness of a combination of both of them were shown.The presented semiautomatic segmentation is capableof fast and accurate segmentations of plant biologicalsample data. Therefore, the results can be used as inputfor the automatic segmentation method proposed byBOLLENBECK et al. [BS08].The presented paper constitutes a contribution tosupport the generation of plant biological 3-D atlases.

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7 FUTURE DIRECTIONSFurther investigations will point toward the evaluationof the presented strategies’ influence to the segmenta-tion results. A user study must be done to validate thetendency of the performed user tests.The results have to be further verified for different seg-mentation scenarios especially in the context of practi-cal use.

8 ACKNOWLEDGEMENTThe Federal Ministry of Education and Research(BMBF) is supporting this research with the grants0313821B and 0313821A. The authors would like tothank their colleagues from the IPK Gatersleben andtheir student assistants for their support and assistance.

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