IEEE Indicon 2005 Conference, Chennai, India, 11-13 Dec. 2005 1

A Novel Approach Towards Accurate Matching of

ParasitesGowrishankar.Sl, C.N.Ravi Kumar2 and G.S.Vijay Kumar3

Abstract-Pattern recognition is characteristic to all livingorganisms. The object which is inspected for the "recognition"process is called a pattern. Medical diagnosis is an example ofpattern recognition problem. By knowing a "KNOWN" we caneliminate all the "UNKNOWNS" in the parasitic sample. Theobjective ofthis work is to match parasites of same type but withapproximately different shape. One should be able to recognizethe parasites ofsame type even ifthere is slight variation in theirshape. In our work we match different types of parasites bychecking their pixel values and their continuity. It is often usefulto have a machine perform pattern recognition. In particular,machines that can read parasites are very cost effective. Amachine that reads the type of parasite can detect many moreparasites than a human being at the same time. Application ofthis nature optimizes the time and enhances the matchingaccuracy. To the best of our knowledge much work is notreported in the field of matching the parasites.Keywords- Parasites, Microorganisms, Image Acquisition,Median filtering, Template matching.

1. INTRODUCTION

O ne of the hallmarks of the human pattern recognitionsystem is its extreme flexibility. As an observer one isable to identify objects under a wide array of conditions

that confound even the most powerful computer vision sys-tems. For example, we can recognize objects at many differ-ent categorical levels.Often, however, it is assumed that objects are first identified

at the entry level defined as the name which is generated ormatched most rapidly to a given object, e.g., "apple" or"bird". This entry-level recognition is performed by Doctorsor Medical Practitioners. [9]While entry-level recognition is certainly an important ele-

ment of everyday recognition, it is not the only level at whichobjects are recognized. We often identify objects at a morespecific level, sometimes referred to as the subordinate level,

Lecturer, Department of Computer Science and Engineering, SriJayacharamarajendra College of Engineering, Mysore, Karnataka, IndiaE-mail: gowru_tiptur@yahoo.com2 Professor and Head, Department ofComputer Science and Engineering, SriJayacharamarajendra College of Engineering, Mysore, Kamataka, IndiaE-mail: kumarcnr@yahoo.com3 Professor and Head, Department of Microbiology, J.S.S Medical College,Mysore, Karnataka. E-mail: gsvijaykumar@yahoo.com

which requires additional perceptual analysis and thus typi-cally take longer than entry-leveljudgments. [9] This is whereour approach comes in handy.Determining the exact match of parasites in a microscopic

picture is quite difficult in conventional methods. In conven-tional method 50 or 100 microlitre of the sample is placed onthe glass slide and stained with suitable stains. Generally, be-cause the microscope image is colorless and transparent, mi-cro cells must be tinted with stains. Usual stains employed aregram stain for ordinary bacteria, Z.N stain for TB bacteria,Leishman stain for blood samples. [8, 13]Almost any source of variability may affect recognition per-

formance, with recent evidence suggesting that the degree towhich a change impairs recognition depends on the categori-cal level ofthe recognitionjudgment. For example, increasingthe similarity between the actual target object and other po-tential target objects typically increases recognition costsacross subordinate level. [6, 7, 10]The template matching problem consists of finding out a

template or pattern in a search image or input image. In prac-tical pattern classification such as medical diagnosis, classifi-cation is done through a given input pattern to one of a finiteset of classes. The representation of each input patterns con-sists of several features. The choice of features to representthe patterns affects the pattern classification, including the ac-curacy, necessary number of examples and cost. [6, 7, 10, 18,19]In template matching, a set of templates or prototypes, one

for each pattern class, is stored in the machine. The input pat-tern (with unknown classification) is compared with the tem-plate of each class, and the classification is based on a preselected matching criterion. In other words, if the input pat-tern matches the template of the ith pattern class better than itmatches any other templates, then the input is classified asfrom the ith pattern class. Usually, for the simplicity ofthe ma-chine, the templates are stored in their raw data form. [9, 16,18, 19].Exact template matching is useless in most ofthe practical

applications. For example inparasite matching two images ofthe same type can be different at different angles. A templatematching algorithm must be based on inexact matching.

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IEEE Indicon 2005 Conference, Chennai, India, 11-13 Dec. 2005 I

2iC- -hena, nda - Dec. 2005

Parasite matching helps to create improved vaccine designsthat pack more disease fighting genetic markers into vaccines.In particular, machines that can read parasites are very cost ef-fective. A machine that reads the type of parasite can detectmany more parasites than a human being in the same time.Application ofthis nature optimizes the time and enhances thematching accuracy.

In the proposed approaches mentioned the required image ofparasites are obtained by CCD camera fixed on the top of themicroscope. The digital Image is then fed into the computerfor further processing. [8]The appearance of the object also depends on its pose; that

is, its position and orientation with respect to the camera. Forexample, a human face will look much different when viewedfrom the side than viewed frontally. The same applies in thecase of parasites. An object detector must be able to accom-

modate all this variation and still distinguish the object fromany other pattern that may occur in the visual world. [20]The Parasitic forms like eggs or cysts have a definite struc-

ture which can be seen under compound microscope. Basedon the visual appearance such parasites can be identified.In some applications, for example, a template is extracted

from the base image which is used as a reference image. [9]The nature of medical images i.e. noise, partial volume ef-

fects, indistinct boundaries makes the problem ofmedical im-age processing quite difficult. Even a slight variation in theway the image is perceived can alter the dosage given. So it isvery important we remove the unwanted objects from the im-age. [9]

II. EXISTING METHODS

Detection of microorganism is difficult in conventionalmethods. It needs special methods like direct visualization ofthe parasitic particles in the sample, detection of viral prod-ucts such as Antigen or Antibody using computer based auto-mated machines like ELISA system, PCR, MB-BACT [12,13]. These are expensive equipments and it is essential for theperson to undergo thorough training. Also the cost per sampleis very high and it cannot be afforded by the economicallyweaker section.ISAP: Image segmentation and analysis in pathology is pub-

lic domain software. This segments the given image intosmaller objects and measures the objects. But it is still costlierthan the proposed algorithnm.

it is quite difficult to recognize and match the parasites ofdifferent shape. Conventional methods are time consuming,which may not be useful in the case of critically ill patients.The drawback of these available tests is sometimes we may

face false negative or false positive results depending on thesensitivity/specificity of the test methodology used.

2.1 Disadvantages of the above existing methods* Parasites belonging to the same family may look different

and this may lead to false prediction of the type of para-site.

* It is a difficult task, for doctors to sit with microscope forlong hours.

* It is highly time consuming.The above drawbacks have necessitated for an easy and effi-

cient approach for matching and recognition of microorgan-isms.

III. PROBLEM DEFINITION

"Our main objective in this research work is to evolve a tem-plate matching algorithm for matching parasites, which hastheflexibility in matching the variability ofthe parasite to bedetected."

IV. PATTERN MATCHING APPROACH

For pattern matching the following approach is followed.Image Acquisition: Digital image capturing is the first step

in any digital image processing applications. The digital im-age capturing system consists of an optical system, the sensorand the digitizer. The sensing device can be a CCD sensor.Here a CCD camera is mounted on top of a microscope andthen the image of the parasite is captured. Thus the capturedparasite image is fed to the computer for processing. The im-age that is fed is used for matching purpose.ofthe digital image formation subsystem will create a defor-

mation or degradation to the digital image due to distortion,noise. Therefore, it is necessary to implement digital imagerestoration and enhancement algorithms in order to reduceborder deformations and degradations. [1, 2, 3].Preprocessing : Image of the various parasites to be

matched are obtained and the image is adjusted to fit a giventone, size, shape to match a desired quality. Preprocessingtechniques include adjusting color or gray scale curve, crop-ping, masking s(cutting out part of an image to be used in acomposition, or toleave a hole in the original image), scalingup (super sampling)/ down (sub sampling), blurring andsharpening, edge enhancement, filtering and antialiasing. [I,2,3]The image Crop process is the process of selecting a small

portion of the image, a sub image, and cutting it away fromthe rest of the image [fig 4(b)]. Noise is the unwanted infor-mation present in the image which is removed using filteringtechniquemany preprocessing techniques filtering is one of the most

important technique. For our work we have employed median

2 IEEE Indicon 2005 Conference, Chennai, India, I I 1 3 Dec. 2005

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Preprwe~sig Feahi Temp_te_hc| aclio | Matc ing

owuFig. 1. Pattem Matching Approach

filtering. Some of the advantages of median filter are Medianfiltering is much less sensitive than the mean to extreme val-ues called outliners. Median filtering is therefore; better ableto remove these outliners without reducing the sharpness ofthe image. The window shape need not be a square. [1, 2, 3]Feature Extraction: Feature extraction plays an important

role in all pattern recognition systems. Feature extraction re-fers to the process of finding a mapping that reduces thedimensionality ofthe patterns. The purpose of feature extrac-tion is to reduce the data by measuring certain "features" or"properties". Here different parasites have to be distin-guished based on their unique structure or shape. To deter-mine to which parasitic family the parasite underconsideration belongs, we must first find the features whichare going to determine this classification. [6, 7, 9, 18]Pattern Classification : Here the task of the classifier is to

use the feature vector of the parasite provided by the featureextractor to assign the parasite to a particular parasitic family.The degree of difficulty of the classification depends on the

variability in the feature values for the microorganisms be-longing to the same family to the difference between the fea-ture values for the microorganism belonging to the differentfamily.Matching: Here the process ofmatching various parasites is

performed

V. METHODOLOGY

The digital image ofthe parasite is obtained by a CCD cam-era mounted on the microscope (fig 2) and stored as BMP file,which can be used for further processing.

In our approach the matching involves thefollowing steps:

Select the images of the various parasites using a digitalcamera and then stored in the system.

Each image will be filtered using filtering techniques to re-move noisy elements. The filtered image is then enhanced byadjusting its contrast, intensity or sharpening. The images arethen converted to gray scale images.Images are scanned from left to right and top to bottom.The above steps are performed for both the input image and

the reference image.If at a particular pixel position (i, j) the value is 1, then the

continuity of the pixel is checked.Reference image pixels are continuously matched with the

input image pixels.If there is a match ofpixel values between the reference im-

age and the input image, then the parasite is found else notfound (fig 5).

Fig.2. Image obtained by CCD Camera

Proposed AlgorithmStep 1: Obtain the Digital Images of various parasites

through CCD camera.Step 2: Crop the interested reference template which needs

to be matched with different input images.Step 3: Maintain a database of the reference images which

needs to be matched with different input images.Step 4: Compare the reference image with the input image

as described above.

Image Acquisition

faund

Fig.3. Steps in Matching Parasite

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IEEE Indicon 2005 Conference, Chennai, India, 1-13 Dec. 2005

VI. EXPERIMENTAL RESULTS

Here to check the validity of our proposed approach, wetook different samples of various parasites. We were able toachieve 1 00% accuracy in matching the related parasites(fig.4).

Fig.4(a). Input Image Fig.4(b). Reference Image

Fig.4(c). Matched ImageFig. 4. Successful Match

Fig.5(a) Input ImageFig.5(b). Reference Image

Fig.5(c) Image not matchedFig.S. Unsuccessful Match

This was validated by the microbiologist (Doctor) as therewas no other algorithm available to the best of our knowl-edge.

Our approach was even able to match when there was slightvariation in the shape; given the pixels have approximatelyequal values.Comparison ofimages from the same imaging modality, but

from different patients will be useful for studying a particularpathology for indexing an image database.

VII. HARDWARE AND SOFTWARE USED

This research work uses digital camera to capture images ofvarious parasites. Images from the camera are then interfacedthrough a port (USB or parallel) and stored as JPEG or BMPcompression format. Other components required are elec-tronic microscope, glass slides, and stains to stain parasites.This research work makes use of various image processing

techniques and the algorithm development is carried out usingthe available image processing toolbox provided by theMATLAB version 6. A graphical user interface is developedto ease the task of pathologist.

VIII. CONCLUSION

The result obtained by the proposed approach gives betterresults when compared to human visual matching.In general template matching requires similarity measures

between the features of a template and the query image. Forobject detection, template matching is performed by matchingthe template at all locations, scales and orientations. If thelikelihood value, obtained from one or several of the match-ing measures, is above a threshold, then a possible matchingof the template is reported.This method helps the doctors to detect the exact intensity

of disease, which in turn helps in deciding the drug dosage.This approach in future can be adopted for the detection of

different types of microorganisms, which is essential for thediagnosis, which in turn helps in accessing the effectivenessof drug treatment.Hence we feel that our work revolutionizes the way the dis-

eases are accurately detected and appropriate treatment isgiven to the patient, so that further complications can be pre-vented. This is just a humble beginning. However, muchwork is needed to be done to cover the entire domain ofmicroorganisms.

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