Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
Malaria Parasite Detection Using Image Processing Rohit. M. Kandari, Harshada. S. Bhere, Shraddha. B. Pophale, Prof. Sonali. J. Mane Bharati Vidyapeeth College of Engineering. Sector-7, C.B.D, Belpada, Navi Mumbai-400614, India. Abstract— Malaria is a serious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year, for which the immediate diagnosis is required in-order to control it. There are various techniques to diagnose malaria of which manual microscopy is the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and likely to have human error leading to erroneous diagnosis, even in experienced hands. If the false detection is done, then the disease can turn into more severe state. So, to overcome this flaw the study about the computerized diagnosis is done in this paper, which will help in immediate detection of the disease to some extent, So that the proper treatment can be given to the malaria patient. This is achieved by using few methods for detecting malaria parasites like Feature extraction. This proposed system help to minimize time as well as provide the accuracy to detect Malaria to some extent. Keywords— Malaria diagnosis, Detection, Infected RBC, Feature Extraction, Severity. I.
INTRODUCTION
Malaria is a disease, transmitted through the bite of a female Anopheles mosquito it should be taken care at the early stage .as inside the human body, the parasite undergoes a complex life cycle in which it grows and reproduces and diagnosis at the later stage can be lifethreatening. During this process, the red blood cells (RBCs) are used as hosts and are destroyed afterwards. Therefore, the ratio of parasite-infected cells to the total number of red blood cells can be used to find the severity of the disease and is an important in selecting the appropriate treatment and drug usage. Malaria is a serious global disease, if it has been not treated or detected from the starting stage it could be more severe or sometime may cause to death. It affects between 350 and 500 million people and causes more than 1 million deaths every year. Malaria is detected manually in which microscopes are used to find the disease by pathologists which sometimes can might lead to false detection or no detection. Currently, clinical diagnosis primarily utilizes microscopy to study the prepared blood smears. However, detection of parasite are difficult and time
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consuming, especially in situations where large numbers of samples require reliable analysis. Hence, it is important to develop an automated image analysis which can be used to identify the uninfected and infected RBCs in a blood smear image. In this paper, Image Processing is done to detect the presence of Malaria Parasite in the RBCs. Malaria Parasite is detected by using Feature Extraction. In this proposed system, fully automated image classification is used to positively identify malaria parasite present in the thin blood smears. II.
EXISTING METHOD
A. Clinical diagnosis: Clinical diagnosis is the least expensive, most commonly used method and is the basis for self-treatment. However, the overlapping of malaria symptoms with other tropical diseases damages its specificity(uniqueness) and therefore encourages the careless judgement use of antimalarial for managing febrile (symptoms of fever) conditions. This practice was understandable in the past when inexpensive and well-tolerated antimalarial were still effective. Accuracy of a clinical diagnosis varies with the level of certain area, malaria season, and age group. No single clinical algorithm is a universal predictor. B. Biologic diagnosis: In 1904, Gustav Giemsa introduced a mixture of methylene blue and eosin stains. Microscopic examination of Giemsa-stained blood smears has subsequently become the Important standard of malaria diagnosis. In the past 50 years, alternative methods became available. Molecular methods, like DNA probes and polymerase chain reaction (PCR) were introduced between 1980s– 1990s. Methods for detecting malaria parasites by fluorescent staining also emerged Detection of malaria pigments by depolarized laser light and mass spectrometry showed limited success. III.
Methodology
Input – The input to our system will be the microscopic image of the human RBC which is be generated in the laboratory. The image is captured by attaching a microscope with the computer. Input consist of two types of images: Page 467
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
1. Malaria Infected RBC image 2. Non-Infected (Healthy person) RBC image Feature Extraction – Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image. Feature Extraction consist of various methods this system uses two methods: Color Extraction – All parasite consists of different frequencies. This frequency help to define the malaria parasite. As the parasite differ, the frequencies of their color also differ from each other which could be used for detecting the malaria parasite. The detection of malaria parasite can be easily done by using the Giemsa stain which helps to give a distinctive appearance. Then the microscopic image of this giemsa strain is given as an input to the system and base on color(stain) the detection of malaria is carried out. Later the noise is removed from the image for further process and based on the intensity of the color of the malaria parasite either the person is infected or not can be determined. Edge Detection - As discussed earlier, all the object obtained from the Feature Extraction are further processed for detecting the edge, an edge is either the boundary between an object and the background or between more than one overlapping objects. There are several types of edge detectors like Sobel Operator, Robert’s Operator, Log Operator, Canny Operator, Prewitt Operator and Zerocross Operator. Sobel Operator - It executes a 2-D spatial directional change in the intensity or color in an image measurement on an image and focuses 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 2D scale image (input gray scale image or the binary image) Corner Detection – This is an approach used within computer vision systems to extract certain kinds of features and conclude the contents of an image. Harris corner – After detecting the boundary of the object it is further analyzed for finding the corners of the object. The object contains information about the feature points detected in a 2-D input image. Severity – This step is carried out to determine the severity of the patient disease. As the ring trophozoite splits itself to form schizont stage, size of the later one is much bigger than the ring trophozoite. Also,
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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. The severity of the disease is calculated as: [8] Severity =
Result – The system will generate the output either the person is infected by malaria or not. If the person is infected, it would display it with the severity of the disease which can further be helpful for the doctors to provide with the same level of medication as per the severity of the person. Algorithm: The Algorithm to detect Malaria is described below: Step 1. Stained blood smear of patient is prepared and fed to program as input. Step 2. Convert the RGB image into the gray scale image. Step 3. Extract out the infected image. Step 4. Remove noise using bwareaopen. Step 5. Area of each of the components is calculated using regionprops on the connected objects. Step 6. Malaria infected or Healthy result is obtained based on this further process are carried out. 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. V.
CONCLUSION
The detection of Malaria parasites is done by pathologists by manually using Microscopes. So, the possibility of false detection due to human error is high and which in turn can result into fatal condition. This system will help to minimizes the human error while detecting the presence of malaria parasites in the blood sample by using image processing and minimize human error by automation. In this proposed system, Feature Extraction is used to detect malaria parasites in images acquired from blood samples. The system is unaffected by the exceptional conditions and can be helped to achieved high percentages of sensitivity, specificity, positive prediction value. And by this one can find the severity of the disease and can be treated from the early stage.
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