Edge Linking Based Method to Detect and Separate Individual C.Elegans Worms in Culture

Nikzad Babaii Rizvandi1, Aleksandra Pizurica1, Wilfried Philips1, Daniel Ochoa1, 2

1 IBBT-IPI-TELIN-UGent, St-pitersneiuwstraat 41, 9000 Gent, Belgium 2 CVR-ESPOL, Km. 30.5 via Perimetral, Guayaquil, Ecuador

{nikzad ,sanja, Philips, dochoa}@telin.ugent.be

Abstract

Nowadays an increasing research interest in the

field of biotechnology has been drawn to achieve reliable information from model organisms. C. elegans nematode worm is one of the major animals. Machine vision analysis of this animal needs to solve many important problems, e.g. detection of each individual in population images, movement patterns of isolated and overlapped worms and so on. In this paper, we describe our recently proposed method with an analysis to show the impact of two major parameters on the method efficiency. Based on our analysis on 255 isolated and overlapped worms, we find that our method prepares the best correct detection (TAR=83%) for o

Th 55=θ and ThN between 15 and 20 pixels. 1. Introduction The automatic analysis of biological images has been the subject of numerous articles [1] in the past; however their effectiveness has been limited by the non-rigid nature of biological structures which decreases the discrimination power of global shape descriptors and consequently the number of detected specimens. This problem is of particular importance in model-organism research where large samples are required to dissect growth, reproductive and motion patterns that had proven correlated to the effect of chemical compounds, temperature variation, food intake and many other experimental variables. Perhaps the most widely used model-organism in the last decades is the C. elegans nematode [2]. This microscopic, featureless worm has been the subject of increasing commercial interest [3] since its genome was completely sequenced [4], opening possibilities for development of new procedures in drug discovery, pollution management and related fields.

To best of our knowledge the earliest work on automatic C. elegans characterization was presented in [5], aimed at examining curvature patterns in the C. elegans head and tail. Segmentation was carried out using thresholding on single-nematode images captured after careful preparation of samples containing one specime. The use of single specimen images continued in [6] when C. elegans behavioral phenotypes classification based on geometric and intensity patterns is introduced. The system described in [7] captures single specimen frames by tracking a moving C. elegans, then geometrical and motion features are extracted and used to classify 15 different strains of mutant nematodes. There are practical reasons to constrain the automatic analysis of C. elegans to one-specimen images. The lack of salient features, complex shape configurations and optical density variations constitute a challenging task for segmentation algorithms especially in the presence of overlapping and clutter. Semi-automatic approaches [8] combining basic segmentation routines and manual drawing do not scale well as the number of specimens in the sample increases. Multi-specimen samples were segmented in [9] by first finding candidate seed regions and then growing and splitting those regions when its shape diverges from a line-type structure in simple overlapping situations. To handle more complex scenes [10] proposes the use of active contour energies to sample the C. elegans body and build a statistical shape model that can be used to classify contours in nematode and non-nematode classes. The effectiveness however depends of having a good initialization which is difficult to achieve in thicker clumps. In this paper we document an improvement on the technique proposed in [11] that is based on skeleton analysis and tree reconstruction concepts to detect individual specimens in clumps, we study the statistical properties the segmentation parameters and apply the estimated

Digital Image Computing: Techniques and Applications

978-0-7695-3456-5/08 $25.00 © 2008 IEEE

DOI 10.1109/DICTA.2008.87

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values for identification of individual C. elegans in population images. 2. C. Elegans Segmentation

To segment the C. elegans we study the continuity of the objects present in the image. We represent such objects using binary skeletons form which pixel connectivity relations can be extracted and the use to estimate a continuity measure. The segmentation strategy is summarized in figure 1.

Pre-processing:

1. High frequency image extraction 2. Global thresholding 3. Morphological skeleton. Splitting skeleton into independent branches: 4. Find Connection-Pixels and Junction-Pixels in the image skeleton. 5. Remove Junction-Pixels and split the skeleton into Connection-Branches and End-Branches. 6. Calculate each Connection-Pixel angle by averaging angles between sequential pixels for N pixels in the related branch starting from that Connection-Pixel using equation 1. Merging branches for individual skeleton reconstruction: 7. Start from the first End-Branch. 8. Assign a new tree to the current End-Branch. 9. Add neighbor branches to the tree last branch if they satisfy |θtreelastbranch - (180+θneighborebranch)| ≤ ·θTh 10. Reconstruct each worm using obtained paths. 11. Go to step (8) for the next End-Branch.

Figure 1. The proposed algorithm

2.1. Pre-processing

The aim of this step is to obtain a suitable skeleton, we use straightforward preprocessing steps that include: Gaussian smoothing with 20*20 pixels mask and σ = 1.0, image subtraction using the original and the blurred image to keep the high frequency pixels (the edges), thresholding on the high frequency image and finally morphological skeletonization. Isolated pixels and noisy branches are removed using closing operations. The components of the skeletons can be classified according to table.1. 2.2 Splitting skeleton The Junction-Pixels sets allow us to split the skeletons that present branches and possibly correspond to overlapping C. elegans. Once the Junction-Pixels are removed the result is a combination of Connection-

Branches and End-Branches. For each Connection-Pixel, an angle (θ ) is calculated by averaging angles between sequential Branch-pixels located inside a window of size ( ThN ) Eq. 1. A diagram of the proposed metric is showed in figure 2.

1... ,14,33,22,1

pixel-Connection −++++

= −

Th

NN

Nθθθθ

θ (1)

The ThN value has to be defined carefully as is must be large enough to avoid the influence of overcome Branch-pixels angle variations near the corresponding Connection-Pixel but also small enough to limit the influence of Branch-pixels located far from the connection-pixel.

Skeleton Pixel Types Junction-Pixel Pixel more than two neighbors. End-Pixel Pixel with only one neighbor. Connection-Pixel

Pixel with only one neighbor after removing the Junction-pixels.

Branch-Pixel Pixel with two neighbors Skeleton Branch Types End-Branch Branch with one End-Pixel and one

Connection-Pixel. Connection-Branch

Branch with two Connection-Pixels.

Table 1: The different type of pixels and branches present in the skeleton.

2.3 Merging branches for individual skeleton

reconstruction For each End-Branch an individual tree is assigned. Each tree is grown up by attaching Connection-Branches. These rules are applicable to this step:

1. The each leave in the tree (rth branch) a new kth branch is attached if they have neighbor Connection-Pixels and they angles satisfy:

thkr θθθ ≤+− )180( (2) Where Thθ (Angle threshold) is user define parameter.

2. The procedure stops when either when an End-Branch is reached or the condition in (2) does not hold.

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Figure 3 explains the steps by an example. By assigning an individual tree to branch 1 the algorithm starts.

Figure 2: The angle calculation procedure of a Connection-Pixel. The ijθ j is the angle between the ith and jth pixels in the same

Connection-Branch. The neighbors of this branch Connection-Pixel with

o481 =θ are Connection-Pixels of branches 2, 3 and 4

with o1262 =θ , o573 −=θ and o1114 −=θ ,

respectively. For oTh 50=θ , only 4θ can satisfy the

condition in (2). So branch 4 is attached to the tree and this branch and /

4θ becomes the tree last branch and the tree last Connection-Pixel, respectively. The tree last Connection-Pixel ( /

4θ ) has two neighbors: Connection-Pixels of branches 5 and 6 with

o1565 =θ and o396 −=θ , respectively. Because none of these angles satisfy the condition in (2), the tree path stops here. To conclude, the tree starting from the first End-Branch has only one path with two members: 1 and 4. The same procedure is executed for the other End-Branches.

3. Calculation of Thθ and ThN parameters and experimental results To measure the algorithm performance, the following criteria are introduced:

• True acceptance rate (TAR): the percentage of correctly detected worms.

• False acceptance rate (FAR): the percentage of non-worm objects detected as worms.

• False rejection rate (FRR): the percentage of undetected correct worms.

The proposed algorithm has a great dependency on the

Thθ and ThN parameters, whose values can significantly affect the performance. To evaluate their influence we test different combinations on5 population images that include 255 isolated and overlapped worms. Table 2, 3 and 4 show the corresponding TAR, FAR and FRR. As can be seen form table 2, the highest True acceptance rate (around 83%) can be obtained for ThN between 15 and 20

pixels and oTh 55=θ . As mentioned before, using

small values for ThN cause the sharp changes near the Junction-pixels affect TAR, while bigger values decreases TAR because of unsuitable far pixels interferences on each connection-branch angle. Table 3 shows that the changes of FRR have the same pattern as changes of TAR, while increasing both ThN and

Thθ always increase the FAR, Table.4. Figure 4 shows the resulting segmentation. In all figures, the colors green, yellow and red refer to the correct detected (TAR), incorrect detected (FAR) and undetected C. Elegans (FRR), respectively.

4. Conclusion In this paper, we described our recently proposed method for detection and separation of C. elegans nematode worms in presence of overlapping in cultures. This method splits the skeletons from Junction-pixels and calculates an angle for each branch using a Window Size threshold ( ThN ). Then it constructs one individual tree for each End-Branch in the skeleton. These trees are grown up based on angle analysis of the branches with their neighbor branches. The algorithm chooses the appropriate branches with angle difference less than an Angle threshold ( Thθ ).Moreover, we showed the impact of two major

parameters ( ThN and Thθ ) on the algorithm efficiency. Applying our method on a database of 5 population images with 255 nematode worms, we realized that the highest True Acceptance Rate (around 83%) can be found using o

Th 55=θ and ThN between 15 and 20 pixels.

5. Acknowledgment

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A.Pizurica is a postdoctoral research fellow of FWO-Flanders. This project is partly supported by Interdisciplinary institute for Broad-Band Technology (IBBT). The images were kindly provided by Devgen Corporation.

ThN (in pixels)

5 10 15 20 25 30 35

Thθ 40 76.8 78.3 81.8 75.9 76.8 73.9 72.1 55 76.2 79.1 83.5 83.1 75.7 74.2 73.3 70 75.3 75.8 76.9 73.2 71.5 70.2 69.7

Table 2: True acceptance rate (TAR)

ThN (in pixels)

5 10 15 20 25 30 35

Thθ 40 21.3 18.5 13.2 14.3 14.1 15 15.1 55 18.3 14.9 10 6.96 10.7 10.6 9.5 70 6.17 16.1 12.2 9.9 10.8 11.7 10.6

Table 3: False rejection rate (FRR)

ThN (in pixels)

5 10 15 20 25 30 35

Thθ 40 1.94 3.21 4.97 9.76 9.15 11.1 12.8 55 5.49 6.01 6.47 9.94 13.6 15.2 17.2 70 6.17 8.12 10.9 16.9 17.7 18.1 19.7

Table 4: False acceptance rate (FAR)

6. References [1] E. Bengtsson, J. Bigun, and T. Gustavsson, “Computerized Cell Image Analysis: Past, Present and Future”, Proceedings of SCIA, Lecture Notes in Computer Science. Vol. 2749. Springer-Verlag, Berlin Heidelberg New York (2003) 395-407.

[2] R.M. Warwick, and R. Price,”Ecological and metabolic studies on free-living nematodes from an estuarine mud-flat”, Estuarine Coastal Mar. Sci. 9 , 257–271.

[3] T. Kaletta, M. Hengartner, “Finding function in novel targets: C. elegans as a model organism”, Nature Reviews Drug Discovery. 5, 2006, 387-399. [4] The C. elegans Sequencing Consortium, “Genome sequence of the nematode C. elegans: a platform for investigating biology”, Science 282, 1998, 2012–2018.

[5] N. Prez de la Blanca, J. Fdez-Valdivia, A. Gomez, and P. Castillo, “Detecting Nematode Features from Digital Images,” Journal of Nematology, vol. 24, no. 2, pp. 289. 297, 1992.

[6] J. Baek, P. Cosman, Z. Feng, J. Silver, and W.R. Schafer, “Using machine vision to analyze and classify C. elegans behavioral phenotypes quantitatively,” Journal of Neuroscience Methods, Vol. 118, pp.9-21, 2002.

Figure 3: An example to explain the procedure: (a) original image (b) its smooth skeleton after removing Junction-Pixels. The Connection-Pixels angles are calculated with

15=ThN pixels and oTh 50=θ . Based on the method outcome,

each of the paths 1-4, 2-3 and 5-6 are individual worms.

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(a) (b)

(c) (d)

Figure 4: The method outcome. The green color represents correct detected worms related to TAR, the red color refers to incorrect detected worms related to FAR and the yellow color describes the correct undetected worms. These figures have been obtained for optimal parameters o

Th 55=θ and 15=Thθ .

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[7] W. Geng, P. Cosman, Ch.C.Berry, Z.Feng, and W.R.Schafer, “Automatic Tracking, Feature Extraction and Classification of C. elegans Phenotypes,” IEEE Transactions on Biomedical Engineering, vol. 51, pp.1811-1820 , Oct. 2004.

[8] J.G. Baguley, L.J. Hyde, and P.A. Montagna, “A semiautomated digital microphotographic approach to measure meiofaunal biomass,” Limnology and Oceanography Methods, vol.2, pp.181-190, 2004.

[9] G. Tsechpenakis, L. Bianchi, D. Metaxas, M. Driscoll, “A novel computational approach for simultaneous tracking and feature extraction of C. elegans populations in fluid environments”, IEEE transactions on biomedical engineering, 55, 2008, 1539-1549.

[10] D. Ochoa, S. Gautama, and B. Vintimilla, “Detection of Individual Specimens in Populations using Contour Energies,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (ACIVS 2007), The Netherlands, Aug. 28-31. 2007, pp. 575.586.

[11] N. Babaii Rizvandi, A.Pizurica, F.Rooms, W.Philips, “Skeleton Analysis of Population Images for Detection of Isolated and Overlapped Nematode C.Elegans,” 16th European Signal Processing Conference (EUSIPCO 2008), Switzerland, August 25-29, 2008.

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