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An Image Processing Approach for Screening of Malaria
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Abstract. Diagnosis of diseases like malaria in the rural area is challenging because of lack of experts. The modern digital
facilities helps in solving this problem. The image based approach enabled by computer technology that helps in diagnosis of
digital slide is termed as digital pathology. In case of malaria, the infection is detected by observing Red Blood Corpuscles
(RBC) manually using microscope. The digital slide preparation of a malaria blood sample and diagnosing the details of disease
is challenging. The development of malaria detection system using image processing technique is a significant part of a modern
digital telepathology. The proposed work presents the image processing approach for classification of malarial from blood
image using YIQ color space. A set of images belonging to malarial and non-malarial classes were processed and a
classification accuracy of 97.5% is obtained.
Keywords: Malaria, Blood Image, Red Blood Corpuscles, YIQ Color Space
1 Introduction
In identifying pathologic conditions or diseases, the medical system is now transforming into digital. Digital pathology area is
very much challenging and interesting, is expected to modernize the existing pathological diagnostic approaches with an
alternate information technology based application. This computerized approach for analysis of various details from blood smear
or tissue involves handling of large database. Several researches are going on in digital pathology applications [1, 2]. In
screening of disease (like malaria or leukemia), the blood sample is analyzed using microscopic approach manually. This manual
approach of diagnosis is time consuming and may lead to inconsistency. Thus this demands trained and experienced technicians
or pathologists. This approach once digitized will reduce the time taken for screening the disease. This will improve the
consistence in diagnosis [3].
The tropical disease Malaria has affected millions of people worldwide and it is seen appearing every year [4]. The parasite
called plasmodium is responsible for the disease. This parasite passes from one human to another by the bite of
infected Anopheles mosquitoes. The injected parasites (called sporozoites) travel through the bloodstream to the liver, where
they mature and release another form called merozoites. The merozoites enter the bloodstream and infects red blood cells. The
parasites get multiplied in the Red Blood Corpuscles (RBC) [5].
Several works have been accomplished for the computerized detection of malarial parasite using HSV color space [3],
extraction of parasite component using gray scale image [6], detection of malaria using texture analysis [7], calculation of RBC
and parasite [8], detection of malaria by extracting cell features [9]. However these approaches have not reached to a 100 %
accuracy.
This paper focuses on processing of blood image for extracting RBCs, later the image converted to YIQ color space. The Q
layer of the YIQ color space is extracted in order to get the parasitic information. Further it is classified as malarial or not based
on rule based approach. This diagnostic tool thus helps in rural places for faster diagnosis.
2 Methodology
The MATLAB code is developed for processing the digital blood image for the detection of malaria. The stages of proposed
algorithm includes the acquisition of blood image, extraction of RBCs, converting the RGB image into YIQ color space,
detection of parasite by extracting the Q layer of the YIQ color space and classifying the image into malarial or non-malarial.
Figure 1 represents the block diagram of developed system for detection of malaria from the blood image.



2.1 Blood Image
The blood smear slides were prepared and observed under the microscope from Olympus (BX51). The blood images with a
resolution of 1280x960 were obtained using Olympus DP25 digital camera which is connected with the computer. The images
are acquired at different magnification and they are shown in the figures 2 (a-c). The images are indicating possibility of
detecting malaria at various magnification using image processing approach. The processing of images with 1000X
magnification is proposed in this work. The blood images with non-malaria and malaria are shown in the figure 3(a) and figure
3(b) respectively. The images were obtained from the Hematology Lab, Kasturba Medical College (KMC), Manipal.


2.2 Red Blood Corpuscles (RBC) Extraction
For the extraction of the RBCs, the image is performed with several operations. The input RGB image is converted to gray scale
and to binary image. The holes in the binary image are filled with the morphological operation (imfill). The blood image
contains platelet and other artifacts, which are considered as unwanted objects in the image. Morphological operation is
performed to eliminate the smaller objects which include platelets. The resulted binary image is shown in the Figure 4 (a). The
binary image is super imposed with the original image so that the RGB image of extracted Red Blood Corpuscles in obtained.
2.3 YIQ Color Space Representation
In the YIQ color space, Y component represent the intensity, I and Q component represents the color information. The YIQ
components are obtained by a linear transformation of the RGB components [10-12]. The resulted color image of RBCs is
converted to YIQ color space for the segmentation of the parasite as shown in the Figure 4 (b).
2.4 Parasite Extraction
The three individual components of YIQ color image are analyzed. The Malarial parasite is more distinguishable from
background objects in Q component of YIQ color Space. The Q component of image is extracted as shown in the Figure 4 ©. A
threshold value is set based on the experimentation. Using a preset threshold on Q layer, the parasite region is segmented as
shown in the Figure 4 (d).
2.5 Classification
The number of ‘on’ pixels in a binary image is calculated to obtain the area of parasitic region. A decision rule is fixed based on
the area feature. Area threshold value is set to decision rule for classifying a given blood image into malarial or non-malarial
sample.
3 Results and Discussion
The developed algorithm removes unwanted objects including platelets from the image efficiently. Better signature of parasite is
found in Q layer of the YIQ color Space. The threshold is set on Q layer for the segmentation of parasite. T
 


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