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Detection of presence of Parasites in Human RBC In Case of Diagnosing Malaria

Using Image Processing

by

Pranati Rakshit HOD, Dept. of CSE

JIS College of Engineering Kalyani, PIN-741235, India [email protected]

Kriti Bhowmik M.Tech Scholar, Dept. of CSE

JIS College of Engineering Kalyani, PIN-741235, India

[email protected]

Abstract— Malaria is the commonest protozoal infestation in human being residing in nearly 3 billion victims across 107 countries and 1-3 million deaths per year round the globe. The disease is generally diagnosed by examining properly stained peripheral blood smear as the malarial parasite particularly invades red blood corpuscles (RBC) of the circulatory system. For this reason, proper analysis of RBC is the most confirmatory diagnosis of malaria. Here in this paper, correct identification of presence of malarial parasite within RBC has been detected and severity of the disease is measured by analyzing the stage (i.e. Ring trophozoite, Merozoite, Schizont etc) of Plasmodium sp., the malarial parasite using different image processing tools and techniques. After several pre-processing activities, area of the infested corpuscle is calculated and Sobel Edge detection method is used to find the boundary of the corpuscles. Then Harris corner points are used to formulate a metric that can determine the severity of the disease. The purpose of this paper is to highlight this medico-technical aspect only.

Keywords-- Harris Corner Detection, Sobel Edge Detection

Operator, Weiner filter, Plasmodium, Red Blood Corpuscles, Ring trophozoite, Schizont

I. INTRODUCTION

Standing in twenty first century, it is quite evident that the modern day pathological science has done a splendid advancement till date. Although there are promising new control and research initiatives, today also malaria remains as dreadful as it had been for centuries. Till now, the main stay of diagnosis of this disease is done by analyzing properly stained blood smear. In this scenario, often the detection part is left upon some inexperienced hand especially in the backward rural places where malaria is very common and all modern day medical amenities have not yet reached properly. Obviously human error factor becomes an important key point in such age old diagnosis process [1]. To avoid this, introduction of some software in the pathological laboratories to detect the disease can be a good alternative to the present scenario. But unfortunate enough to say that no such work has been carried off till now to apply the concepts of engineering science in diagnosing malaria. Here in this paper, we have introduced a program which successfully detects presence of Malaria by analyzing infected blood smear image and hence diagnosing the stage and severity of the disease with the help of the proposed metric. Some basic information about the disease is given below which is essential to get into the technical aspect of this work.

I. What is a malarial parasite? Malaria is a zoonotic febrile disease caused by bite of infected female [1] Anophelis mosquito. The organism that creates the disease is a Plasmodium genus of protozoa which are

i. Plasmodium vivax ii. Plasmodium falciparam

iii. Plasmodium ovale iv. Plasmodium malariae

II. Different stages of the parasite's life cycle: Malarial parasite passes its life cycle in 2 different hosts [2].

1. Man: The asexual cycle of the parasite is passed in human body. Parasite resides in Liver and RBC and reproduces asexually. Hence man is the intermediate host.

2. Mosquito: The sexual cycle of the parasite is passed in mosquitoes (sporozoites are produced from male and female gametocyte). Thus female anopheline mosquito is the definitive host of malarial parasite.

III. The Life Cycle of Plasmodium: The malarial parasite, Plasmodium, is a microscopic, single-cell blood organism, or 'protozoan' [3]. It lives as a parasite in other organisms, namely man and mosquito. The parasite is solely responsible for the tropical disease malaria. The Plasmodium parasite can grow up in a single species of mosquito named Anopheles, which is the only species capable of serving as host for it. This small single-cell organism [4] has three to four different forms. Each stage of parasite is subjected to live in some certain place.

• The gametocyte is the form that infects the mosquito and reproduces itself, as if it were of both sexes. When the mosquito sucks blood containing gametocytes, these pass into the salivary gland of the mosquito, where they get changed into a new form, the sporozoite. The infection then moves on.

Proceedings of the 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013)

978-1-4673-6101-9/13/$31.00 ©2013 IEEE 329

1 2 1 0 0 0 -1 -2 -1

• The sporozoite is then passed on to man when the mosquito bites, injecting its saliva into the tiny blood vessels of a human being. The sporozoite travels to the liver with the blood and enters the liver cells. In the liver some of the sporozoites divide (tachysporozoites) and become thousands of merozoites.

• The merozoites are released from the liver to the

blood where they enter into red blood corpuscles. Some of these are transformed to ring-formed trophozoites [4] that split again to form schizonts.

-1 0 1 -2 0 2 -1 0 1

Gx Gy

Fig. 1 Masks used by Sobel Operator

The x-coordinate is defined as increasing in the right-direction, and the y-coordinate is defined as increasing in the down- direction. The resulting gradient approximations can be combined to give the gradient magnitude, using:

2 2 • The schizonts burst the RBCs at a certain moment,

releasing the merozoites. This release is associated with the violent spiky rise in temperature with chill and rigor.

The trophozoites that are left over during division can, in the course of the next few days, develop into the sexual form, the gametocyte, which can be taken up by a female blood-sucking mosquito and thus another cycle is started.

II. METHODOLOGY

A. Preprocessing: Colored blood smear image is converted into binary image. The image is then complemented. Weiner method is used to remove noise by adaptive filtering. Then small unwanted regions are removed from the diagram to obtain a clearer view of the region of interest i.e. the red blood cells in the blood sample. The obtained image is then used for further processing.

B. Edge Detection: An edge is either the boundary between an object and the background or between more than one overlapping objects. Edge detection is the technique of identifying discontinuities in an image. Several variables are involved in selection of an edge detection operator such as edge orientation, noise environment edge structure etc [6]. The geometry of the operator determines the characteristic direction in which it is most sensitive to edges. There are several types of edge detectors like Sobel Operator, Robert’s Operator, Canny Operator, LoG Operator, Zerocross Operator and Prewitt Operator. Among all these, Sobel Method is described below:

Sobel Operator: It is a 3×3 gradient edge detector. This operator enumerates 2-D spatial gradient on an image and so emphasizes regions of high spatial frequency that correspond to edges. It is used to find the approximate absolute gradient magnitude at each point of an input gray scale image. Mathematically, Sobel Operator uses two 3×3 matrix which are convolved with the original image to calculate approximations of the derivatives - one for horizontal changes, and one for vertical changes [7]. The horizontal and vertical derivative approximations of Sobel operator are given in next section:

Using the above equation, the gradient's direction can also be calculated which is discussed in the following equation:

Where, for example, Θ is 0 for a vertical edge which is darker on the right side. Reason behind using Sobel Operator over here: As Sobel operator executes a 2-D spatial gradient measurement on an image and emphasizes on regions of high spatial frequency that are related to the edges, it is used to find the approximate absolute gradient magnitude at each point of an input gray scale image [6]. That’s why in detection of edge of biomedical images, Sobel operator is used. C. Harris Corner Detection: Harris corner detector is based on the local auto-correlation function of a signal which measures the local changes of the signal with patches shifted by a small amount in different directions [5]. Given a shift (∆x, ∆y) to a point (x, y) the auto-correlation function is defined as: c(x, y) = ∑w[I(xi, yi)-I(xi+∆x, yi+∆y)]2

…… (1) Where I (xi, yi) represents the image function for (xi, yi) points in the window W centered around (x, y). The Gaussian window is defined as where σ defines the width of the window. The shifted image is approximated by a Taylor expansion truncated to the first order terms: I(xi+∆x, yi+∆y) ≈ [I(xi, yi)+[Ix(xi, yi)Iy(xi, yi)]] [∆x ∆y]

…… (2) Where Ix(xi, yi) and Iy(xi, yi) indicate the partial derivatives with respect to xi and yi respectively. With a filter like [-1, 0, 1] and [-1, 0, 1] T, the partial derivative can be calculated from the image by substituting Eqn. (2) in Eqn. (1).

…… (3) C(x, y) is the auto-correlation matrix that captures the intensity structure of the local neighborhood. For α1 and α2 be Eigen values of C(x, y), three cases may arise:

1. Both Eigen values are small signifying uniform region (constant intensity). 2. Both Eigen values are high signifying Interest point (corner) 3. One Eigen value is high signifying contour (edge)


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To find out the points of interest, Characterize corner response H(x, y) by Eigen values of C(x, y).

• C(x, y) is symmetric and positive definite that is α1

and α2 are >0 • α1 α2 = det (C(x, y)) = AC –B2 α1 + α2 = trace(C(x, y)) = A + C • Harris suggested: the corner response

HcornerResponse= α1 α2 – 0.04(α1 + α2)2 Finally, it is needed to find out corner points as local maxima of the corner response

D. Region Selection: This calculates some properties of image regions. The properties can be a comma-separated list of strings [10]. The single string can be ‘all’, or the string ‘basic’. If the property value is mentioned as string ‘all’, Regionprops compute all the shape measurements like Area, Bounding Box, Centroid, ConvexHull, ConvexArea etc. If called with a grayscale image, regionprops also returns the pixel value measurements like Max Intensity, Min Intensity, Weighted Centroid, Mean Intensity, Pixel Values etc. If ‘properties’ is not explicitly mentioned or if it is inrerpreted as the string 'basic', the function ‘regionprops’ computes only the 'Area', 'Centroid', and 'BoundingBox' measurements. Here extensive use of the property ‘Area’ has been done. 'Area' is a scalar value which represents the actual number of pixels in the region. If the image contains discontinuous regions, ‘regionprops’ returns unexpected results. Here in this piece of work, Area of each of the connected components are measured to calculate the required metric.

III. PROPOSED METHOD

A. Data Acquisition: A blood film is produced by pricking pulp of any finger

by surgical needle in aseptic condition. Drop of blood not larger than pin head taken on a grease-free glass slide at half inch from the right side. Another glass slide end held at 45° touching the blood drop is lowered to 35° then pushed gently to the left till blood is exhausted giving a tailing effect. Then the slide is air dried and labeled. As malaria is suspected, thick and thin both films are to be stained and a rapid Romanowski’s staining method is adopted. The stained film is examined under high power oil immersion microscope. This photograph is fed to computer and is ready to be used as the input to the program.

B. Finding the Region of Interest i.e. RBCs:

Malaria is generally detected by analyzing change in the normal morphology of RBCs. Though human blood circulation system consists of WBCs and platelets along with RBCs, finding a small area with only RBCs in high power field (HPF) of oil immersion microscope [12] is an absolute possible and very common phenomenon as the ratio of RBC to WBC in normal blood ranges from nearly 1000:1 to 2000:1 in human being. The cell count in normal physiological state reveals this fact. In male, RBC count is 4.6 million - 6.2 million per cubic mm and in female 4.2 million-5.4 million per cubic mm whereas the count of WBC is 4000 - 11000 per cubic mm. From the perspective of presence in blood, RBC makes up 36-50% of our blood depending on height, weight & sex while WBC contributes nearly 1% of blood volume. Thus finding a blood smear in

an HPF with only RBCs is a common possibility. What we need to do is to select the field accordingly to find our region of interest i.e. RBC in this case.

On other hand, Platelets are tiny bodies with 2 to 4 microns in diameter which are about half the size of an RBC [13]. Also while stained with May-Grunwald Giemsa stain, they look like light purple granules which are discarded from the blood smear diagram during background lightening and preprocessing the image. This way by discarding WBCs and platelets from blood smear image, we can focus on the region of interest (i.e. RBCs) of our work.

C. Algorithm: The Algorithm to detect Malaria is described below: Step 1. Stained blood smear of malaria patient is prepared and fed to program as input. Step 2. Binarise the image and apply Binary area open for removing the small objects. Step 3. Number of similar components is detected using bwconncomp. Step 4. Area of each of the components is calculated using regionprops on the connected objects. Step 5. Detected corpuscles are displayed one by one and then corresponding surface area is displayed in matlab command window. Step 6. RBCs carrying inclusion body within are screened out for going through further steps. Step 7. Sobel edge detection algorithm is applied on the obtained image to detect malarial parasite. Step 8. Harris Corner Detection Algorithm is applied. Step 9. All the Harris corner detected pixel positions are computed. From these values, a metic is formulated which can detect stage of malaria.

IV. RESULT AND DISCUSSION

MATLAB 7.14.0.739 Software is extensively used for the study of detecting malaria. In the original programme, we considered several samples of infected blood in different stages of malaria. Here in this section, two result sets (one of ring trophozoite and one of schizont state) are described. I. Result set for Malaria sample1 (Ring trophozoite stage of malaria):

Fig. 2 Original malarial blood smear

Original image is binarised to get the next image

Fig. 3 Binarised blood smear

Inclusion bodies detected inside RBCs are shown in pink.


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Fig. 4 Blood smear of malaria after transforming it to RGB

After this, the inclusion bodies are shown along with the background RBCs

Fig. 5 Infested corpuscles are shown along with the Background smaller ones

Inclusion bodies are edge detected by Sobel method

Fig. 6 Infestation of RBCs detected by Sobel Operator

Harris corner point is applied on those bodies.

Fig. 7 Harris corner points detected on the infestation

Here 15 corner points have been detected. These are: (110, 90), (94, 95), (61,103), (89, 109), (71, 113), (100, 117), (82, 119), (91, 119), (122, 128), (99, 132), (117, 138), (89, 142), (98, 145), (93, 154), (105, 155)

II. Result set for Malaria sample2 (Schizont stage of malaria):

Fig. 8 Original malarial blood smear Original image is binarised to get the next image

Fig. 9 Binarised blood smear The above image is complemented to get the next image.

Fig. 10 Complemented and preprocessed image of the binarised blood smear Inclusion bodies are detected inside RBC, which are shown in pink.

Fig. 11 Blood smear of malaria after transforming it to RGB


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Stage of malaria

Area of schizont

/ring trophozoite

(a)

Total count of detected points (p)

Metric value=(p/a)*

100

Mature Schizont

Sample – 1

618

5

0.80

Mature Schizont

Sample – 2

2134

15

0.70

Early

Schizont Sample – 3

5293

14

0.26

Ring trophozoite Sample – 1

( 2190 +

1796 + 2025 )

15

0.24


5021

11

0.21


4081

9

0.22

Showing inclusion bodies along with the background RBCs

Fig. 12 Infested corpuscles along with the Background smaller ones

Inclusion bodies are edge detected by Sobel method

Fig. 13 Infestation of RBCs detected by Sobel Operator

Harris corner point is applied on those bodies.

Fig. 14 Harris corner points detected on the infestation

Again 15 corner points are detected. These are: (267, 35), (267, 56), (307, 59), (257, 70), (252, 78), (309, 79), (298, 84), (179, 168), (199, 194), (98, 198), (134, 202), (123, 208), (82, 221), (144, 223), (84, 240)

After detecting Harris Corner point of the infected part in an RBC in several malarial blood samples, we finally come to a conclusion. Before discussing that point, we like to put forward the biological and mathematical aspect.

Biological Aspect: As the infected mosquito bites a healthy person, sporozoite (a particular stage in the life cycle of malarial parasite) is injected to human blood. After passing 10-12 days in the blood circulatory system, the malarial parasite enters into RBCs as in the form of Ring Trophozoite (a ring like structure) and rapidly divides itself to form the Schizont stage (rossette shape). Detecting the presence of these ring trophozoites, early and late schizont inside RBCs is one of the important confirmatory diagnosis of malaria. That’s why here in the result set, we have shown 2 samples of malaria; one is of Ring Trophozoite and another is of Schizont state.

Mathematical Analysis: As the ring trophozoite splits itself to form schizont stage, size of the later one is much bigger than the ring trophozoite. Also the schizont form of the parasite is denser and clumsier than ring trophozoite. For this reason, the number of detected Harris corner points per area is formulated as the metric to determine the stage of the malarial parasite within RBC. Here the metric is defined as

M = (p/a)*100 Where p= Total count of detected points a= Area of schizont / ring trophozoite This final result set is discussed in tabular form below:

TABLE I RESULT SET FOR DIFFERENT SAMPLES OF MALARIA

Observation: • In Ring Tophozoite value is 0.24, 0.21, 0.22 Early

Schizont value is 0.26 whereas for Mature Schizont value reaches 0.70, 0.80

• The metric value tends to 1 as the disease transforms from early to late stage.

• Thus it is observed that, lesser value of metric is obtained in earlier stage of malaria and the value of the metric is increased during the later stages of the disease.


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Statistical Analysis: From Figure 5, Result set can be tabulated as follows:

Fig. 15. Statistical analysis of the malaria affected blood sample.

Here, True Positive: Malarial parasite is present and the test

result is +ve. Here in the sample, number of actually diseased RBCs in Fig 5 is 3. Finding true positive value in statistical analysis of the blood smear image means that malaria is actually present in the patient.

False Positive: Malarial parasite is not present but the test result is +ve. It is the count of normal RBCs or other blood corpuscles such as WBCs mistakenly diagnosed as the diseased one. The count is 0 in this case. Finding this erroneous case can be restricted by choosing the proper field with only RBCs under High Power Field oil immersion microscope.

True Negative: Malarial parasite is not present and the test result is -ve. Here in this sample, number of such RBCs in Fig 5 is 15. This value denotes the normal RBC count in blood smear.

False Negative: Malarial parasite is present but the test result is -ve. It is the number of diseased RBCs which are not properly diagnosed. Here, count is 0 in this case.

V. CONCLUSION

This present piece of work is innovative of its kind where some infected blood samples are analyzed to determine stage of the disease malaria. Apart from detecting the presence of diseased RBCs in the blood smear, it has been seen that the value of the metric we proposed here is lower for earlier stages of malaria and it increases with the maturity of malarial parasite within RBC. Thus apart from detecting malaria, severity of the disease in human body can also be diagnosed in an efficient way through this technique.

TABLE II RESULT SET FOR DIFFERENT BLOOD SAMPLES

Sam

ple RBC

count in the

sample

True +ve (a)

True -ve (b)

False -ve (c)

False +ve (d)

Accuracy (a+b) /

(a+b+c+d)* 100

Over all accuracy of the system

1 18 3 15 0 0 100%

97.73 %

2 13 1 12 0 0 100% 3 11 2 9 0 0 100% 4 13 2 10 0 1 92.307% 5 12 0 12 0 0 100 % 6 17 2 14 1 0 94.117%

From Table II it is seen that 6 samples are analyzed using the proposed system. For the confirmatory detection of the disease in each sample (out of the above mentioned 6 cases), nearly 100 HPFs are needed to be analyzed. That means approximately (6 * 100) i.e. 600 field images are needed to be examined before coming to the conclusion.

Here in this case, for our convenience we have shown only 2 of such analysis of blood smear image in Results and Discussion section. And the overall calculation says that in 97.73% cases the diagnosis of Malaria is correct and goes at par the result detected by pathologists.

ACKNOWLEDGEMENT

We express our deepest gratitude to Dr. Goutam Bhowmik and Dr. Dipankar Ghosh who helped us immensely to build a clear conception regarding the topic. Also, based on their diagnosis and feedback, the obtained result can be verified against the actual report and thus accuracy [11] of the system has been measured.

REFERENCES

[1] Nicholas A. Boon, Nicki R. Colledge, Brian R. Walker, John A. A.

Hunter, “Davidson’s Principles and Practice of Medicine”. 20th ed., published by Churchill Livingstone (Elsiever), 2006, pp. 342-348.

[2] William F. Ganong, “Review of Medical Physiology”, 18th ed., published by Prentice-Hall International Inc., 1997, pp. 496–498.

[3] K.D.Chatterjee, “Parasitology”, 12th edition, published by Chattergy Medical publishers, pp. 72-84.

[4] www.netdoctor.co.uk/travel/diseases/life_cycle_of_the_malarial_parasite.htm

[5] http://docs.opencv.org/doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.html

[6] http://homepages.inf.ed.ac.uk/rbf/HIPR2/sobel.htm [7] Raman Maini, Dr. Himanshu Aggarwal, “Study and Comparison

of Various Image Edge Detection Techniques” [8] www.mathworks.com/matlabcentral/fileexchange/9303-sobel-edge-

detection [9] http://www.onhealth.com/malaria/article.htm [10] www.mathworks.in/help/images/ref/regionprops.html [11] https://en.wikipedia.org/wiki/Accuracy_and_precision [12]http://www.microscope-microscope.org/basic/microscope-

glossary.htm [13]http://www.vetmed.vt.edu/education/curriculum/vm8054/Labs/Lab6/

Lab6.htm


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