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A PRELIMINARY STUDY FOR CONSTRUCTING A

COMPUTATIONAL PROCEDURE FOR NEMATODES

IDENTIFICATION BASED ON MORPHOLOGICAL ASPECTS

1Luis A.M. Palhares de Melo,

2Rogerio C. Bastos

1CENARGEN - EMBRAPA - Centro Nacional de Recursos Genticos e

Biotecnologia, SAIN Parque Rural Final W5 Norte, 70.770-910, Braslia-

DF, BRASIL, e-mail: palhares@cenargen.embrapa.br,

2

UFSC -Universidade Federal de Santa Catarina, Ps-Graduao em Cincia da

Computao, Campus Trindade, 88.040-900, Florianpolis-SC, BRASIL

e-mail: rogerio@eps.ufsc.br

Abstract: Nematodes are vermiform animals of long and thin body and

slender at the two extremities. They have great economic importance

because many are plant parasitic and very difficult to erradicate once

established in the soil. A prerequisite to combating their agricultural impact

is correct identification of species.

We present a preliminary study for constructing a computational procedure

for nematode identification (in terms of taxonomic classification) based onmorphological aspects of various organs (stylet, ovary, tail, esophagus, etc).

The procedure is an artificial intelligent application involving concepts of

computer vision and pattern recognition (an automated system that

"visually recognizes nematode's structures).

Keywords: Computer vision; Pattern recognition; Nematode

1 Characterizing nematodes

Nematodes are vermiform animals of long and thin body and slender on the two extremities, thatare found everywhere in the planet where life can exist. Beside their typically cylindrical format,

nematodes present a complete digestive duct with mouth and anus, and bilateral symmetry. Various

organs compose nematode's body including stylet, metacorpus, isthmus, ovary, esophagus and annulis.

Figure 1. Drawings of males and second-stage juveniles ofMeloidogyne paranaensis n. sp. A)

Anterior portion of male. B) Male tail, lateral view. (Carneiro (1996))

Many kinds of nematodes attack most plants of economic importance. Different species cause

different types of damage, for instance, some cause seed destruction, others a drastic reduction of the

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productivity and quality of planted land. The first step in reducing nematode damage is the correct

diagnosis of the problem and of the nematode causing it.

2 Development of computer identification programs

Boag et al. (1988) comment that most nematode species are identified using a number of

morphometric and morphological characters and the morphometric characters are particularly suitable

for measurement and analysis by a computer. Also, they point out some computer identification

programs based on the nematode's morphometric characters, have already been written for identifying

genus Helycotilenchus and Longidorus. Peet et al. (1990) developed a digital imaging system for

measuring various physical characteristics of individual nematodes and for comparing groups of

nematodes. La Blanca et al. (1992) and Valdivia et al. (1992) developed computational procedures

for estimating and calibrating nematode features from digital images. As they point out, feature

identification from digitized microscopic images has been used for more than 10 years but is

relatively new in nematology. Bravo & Roca (1994) wrote a program for identifying genusXiphinema

using dBASE IV software and Robbins & Brown (1994) did the same to genus Longidorus using

LOTUS.

3 A computational procedure for identifying nematodes

3.1 The main steps to proceed identification

To process morphological (and morphometrical) characters automatically we have to take into

account aspects of computer vision and pattern recognition where the source data to be initially

processed are pictures contained in digital images. We started a preliminary study with this idea in

mind and developed a software prototype (using C++ language) that tries to recognize nematodes (in

terms of taxonomy) based only on stylets morphology aspect. We assumed that only stylets shape issufficient to correctly identify the nematode although we know that this is not the case.

The basic idea is recognition based on template matching technique. A database was built using

contour sketches of stylets of nematodes of known taxonomy. Having an unknown nematode digitized

image, the computational procedure must localize the stylet, and constructs a sketch (the

morphological aspect considered). Then by template matching, based on fuzzy sets (Zadeh (1965),

Shaout & Suk (1992)) the procedure compares these sketch against all stylets sketches stored

previously on the database. A distance (similarity measure) ranging from zero to one is generated for

every comparison. A distance zero indicates that sketchs are strictly identical, and a distance one that

they are completely different from each other.

In figure 2 we see the results produced by the program after storing a new stylets shape sketch in

the database. Initially an unknown digitized nematode image focusing stylet is presented to the

program (window NEMAT07.BMP). The program then performs image segmentation generating awindow (SEGMENT.BMP). We have used a generic segmentation algorithm proposed by Horowitz

& Pavlidis (1976) but other segmentation techiniques could and must be implemented to improve

segmentation performance.

The automated procedure that locates the stylets position was not considered at this stage.

Therefore, to locate the stylet in digitized images, the programs operator points out the region

corresponding to it with the help of mouse in window SEGMENT.BMP. So the program isolates

stylet and shows it in window ESTILETE.BMP. Once the stylet is isolated, the program produces

its sketch based on an very simple algorithm of polygonal approximation proposed by Pavilids &

Horowitz (1974) that must be further complemeted by others that improve the final layout sketch.

Finally, the window Identificar Estilete registers generic information (including taxonomic

propreties) about the neamtode. These data and the stylets sketch are stored in the database.

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Figure 2. Results of entire process of registering a new nematode in the database.

3.2 What must the computational procedure anserws ?

We have implemented a single test to evaluate the feasibility of the process. In our test we stored

in the database three stylets sketches of three distinct nematodes. They were stored in files

TEMPL01, TEMPL02 and TEMPL03. In file PADREC was stored a stylet's sketch of an unknown

nematode. The test consists of calculating the distance measure (based on fuzzy sets) between

PADREC and TEMPL01, TEMPL02 and TEMPL03 respectively (shown in figure 3).

Figure 3. Stylets sketches used in the test of the program.

Figure 4. Template matching between PADREC and TEMPL01, TEMPL02 and TEMPL03

The question the program answers is: Which of the three pictures (TEMPL01, TEMPL02,

TEMPL03) has more resemblance with PADREC ?. The distance between PADREC and TEMPL01,

TEMPL02 and TEMPL03 calculated by the program is respectively 0.261199, 0.351007 and

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0.364595 (see figure 4). PADREC will be assumed as having the features of nematode with stylet's

sketch TEMPL01 (0.261199, the least distance).

Yet in figure 4 we can see that numeric distance between PADREC and TEMPL02 and between

PADREC and TEMPL03 are too close (0.351007 and 0.364595) and it does not reflect semantically

the situation. That is, if distance between PADREC and TEMPL02 is quite the same as distance

between PADREC and TEMPL03 it would be expected that TEMPL02 and TEMPL03 are closely

identical and that's not the case. To manage this problem, we must forward in our study, redefine the

fuzzy sets and general conditions that support our template matching scheme.

4 Conclusions

With this preliminary study we have shown that a computational procedure for identifying

nematodes based on aspects of computer vision and pattern recognition is feasible. Only

morphological aspects were taken into account in this work. We later intend to improve the power of

the computational procedure, considering morphometric aspects, as well as the management of

spatial relations between organs, which compound some nematode identification key, like "esophagus

overlaps intestine ventrally", "procorpus and metacorpus not swollen and combined into a largevalvular bulb", "lateral field with 4 incisures", among others.

5 References

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identification of plant-parasitic nematodes. In: Nematode Identification and Expert System

Technology. 9-15. Plenum Press, New York, London.

Bravo, M.A. & F. Roca (1994). Using dBASE for Identification of Species of the Genus Xiphinema.

In: 22nd International Symposium Proceedings of the European Society of Nematologists, Ghent,

Belgium, Aug/1994.Carneiro, R.M.D.G., R.G. Carneiro, I.M.O. Abrantes, M.S.N.A. Santos & M.R.A. Almeida (1996).

Meloidogyne paranaensis n. sp. (Nemata: Meloidogynidae), A Root-Knot Nematode Parasitizing

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Horowitz, S.L. & T. Pavlidis (1976). Picture Segmentation by a Tree Traversal Algorithm. In:Journal

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La Blanca, N.P., J.F. Valdivia, P. Castillo & A.G. Barcina (1992). Detecting Nematode Features from

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Melo, L.A.M.P. (1996). A Model for Nematode Identification based on Stylet Structure. Msc Thesis.

Federal University of Santa Catarina- Brazil. (In portuguese)

Pavlidis, T. & S.L. Horowitz (1974). Segmentation of Plane Curves. In: IEEE Transactions on

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Peet, F.G., T.S. Panesar, T.S. Sahota & J.R. Sutherland (1990). A Digital Image Analysis System forComparing Groups of Small Nematodes. In:Journal of Nematology22(3) : 407-413.

Robbins, R.T. & D.J.F. Brown (1994). A Computer Program "LONG-SORT" to Assist with the

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Shaout, A. & M. Suk (1992). Distance Measure for Attributed Fuzzy Tournaments. In: IEE

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Valdivia, J.F., N.P. La Blanca, P. Castillo & A.G. Barcina (1992). Line Detection and Texture

Analysis for Automatic Nematode Identification. In:Journal of Nematology24 (4) : 571-577.

Zadeh, L.A. (1965). Fuzzy Sets. In :Information and Control 8 : 338-353