Automatic Detection of Malaria Parasite from Blood Images

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Volume 1, No. 3, May 2012 ISSN 2278-1080 The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at http://www.journalofcomputerscience.

1 Volume 1, No. 3, May 2012 ISSN The International Journal of Computer Science & Applications (TIJCSA) RESEARCH PAPER Available Online at Automatic Detection of Malaria Parasite from Blood Images Deepali A. Ghate 1, Prof. Chaya Jadhav 2 Department of Computer, D. Y. Patil, College of Engineering, Pimpri Pune, Maharashtra India. deepali.ghate@gmail.com Assistant Professor, Department of Computer, D. Y. Patil, College of Engineering, Pimpri Pune, Maharashtra India. chaya_jadhav@yahoo.com Abstract Malaria is a life threatening disease so rapid, accurate diagnosis is required to control the disease. The detection of Malaria parasites is done by pathologists manually using microscopes. So, the chances of false detection due to human error are high, which in turn can result into fatal condition. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. This study aims to explore the possibility of computerized diagnosis of malaria and to develop a novel image processing algorithm to reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. This project curbs the human error while detecting the presence of malaria parasites in the blood sample by using image processing and automation. We achieved this goal using Image Segmentation and Thinning techniques to detect malaria parasites in images acquired from Giemsa stained peripheral blood samples. We also built the system in a robust manner so that it is unaffected by the exceptional conditions and achieved high percentages of sensitivity, specificity, positive prediction and negative prediction values. Keywords- Malaria diagnosis, thinning algorith threasholding, labelling algorithm. 1. Introduction Malaria is a life-threatening parasitic disease, caused by the protozoan parasites of the genus Plasmodium and is transmitted through the bite of a female Anopheles mosquito. Inside the human body, the parasite undergoes a complex life cycle in which it grows and reproduces. During this process, the red blood cells (RBCs) are used as hosts and are destroyed afterwards. Hence, the ratio of parasite-infected cells to the total number of red blood cells called parasitaemia can be used as a measure of infection severity and is an important determinant in selecting the appropriate treatment and drug dose [3]. There are four species of the plasmodium parasite infect humans, which are Plasmodium falciparu Plasmodium vivax, Plasmodium ovale, and Plasmodium malariae. The most serious and 66

2 virulent forms of the disease are caused by Plasmodium falciparum which contributes to the majority of deaths associated with the disease. Others only cause milder diseases in humans. With the similar characteristics, P. ovale appears mainly in tropical Africa. P. falciparum is most common in tropical and subtropical areas. It causes the most dangerous and malignant form of malaria without relapses and contributes to the majority of deaths associated with the disease. P. malariae is also widely distributed but much less than P. vivax or.falciparum [4]. 2. System Architecture System architecture used for Malaria parasite detection involves following steps: Threasholding, gray scale image conversion, binary image, edge detection algorith thinning of binary image, labelling algorithm. Block diagram of system architecture is shown in Figure 1, Figure 1: System Block Architecture 2.1 Rbc Counting Red Blood Cell (RBC) extraction is a very important and vital step in RBC counting. As there is a possibility of other elements to be present on the smear, only RBCs need to be extracted. RBCs are extracted based on their specific color. RBCs are normally red in color and circular in shape. The cells other than RBCs are removed from the image. The obtained image will be consisting of only extracted RBCs. Extraction of RBCs is expressed mathematically in equation below: if, 170 x( n,1) 255 x( n, i) 150 x( n,2) x( n,3) 220 g( n) = 255 Otherwise & & Where, g(n) is m rows by n columns 24 bit color image obtained after RBC extraction, 67

x(n,l) is the 8 bit red color plane in x(n), x(n,2) is the 8 bit green color plane in x(n), x(n,3) is the 8 bit blue color plane in x(n), For further processing the image need to be converted to gray

3 x(n,l) is the 8 bit red color plane in x(n), x(n,2) is the 8 bit green color plane in x(n), x(n,3) is the 8 bit blue color plane in x(n), For further processing the image need to be converted to gray color, whereby the image, which was in true color previously having 24-bit depth, will get converted to gray color having 8-bit depth. During conversion to gray color few cells lose their contents and become hallow, these cells need to be filled. Ideally the RBCs, which are pale in color, are abnormal and can be considered as color abnormality. But for the purpose of counting number of RBCs these hallowed cells need to be filled up. The lost contents of the cell are filled by flood fill technique. After converting to gray and filling the lost contents the image need to be smoothed. This is done with the Average filter, which is a linear spatial filter sometimes it is called as low pass filter also. By replacing the value of every pixel in an image by the average of the gray levels in the neighborhood defined by the filter mask, this process results in an image with reduced sharp transitions in the gray levels. Because the random noise is typically consist of sharp transitions in the gray levels. It not only removes noise but smoothes the edges for better edge identification. Mean filtering is a simple, intuitive and easy to implement method of smoothing images, i.e., reducing the amount of intensity variation between one pixel and the next. The image, which was gray scale image, is now converted to binary level image as shown in figure 2 with the help of Adaptive Thresholding or Dynamic Thresholding. Figure 2: Binary image converted from a smoothed gray scale image. During so many Image processing operations on the image, some of the cells may broke down to smaller pieces. And counting being an automated process may count the small pieces and may lead to wrong counting, for the same those cells need to be removed as shown in figure 3. These cells can be removed based on the criteria of the area being occupied by these cells. Area of each cell is counted and according those cells which are not suitable for counting based on the threshold is removed. 68

Figure 3: Image transformed into thinned image from a thick image Counting of cells is done by first labeling the connected components and then counting the elements with same pattern.

4 Figure 3: Image transformed into thinned image from a thick image Counting of cells is done by first labeling the connected components and then counting the elements with same pattern. This can be done by using the concept of 4-connected or 8- connected components. Two foreground pixels p and q are said to be 4-connected if there exists a 4-connected path between them consisting of entirely of foreground pixels as shown in figures above and below figures. The same p and q are said to be 8-connected if there exists a 8-connected path between. The 8connectivity will be gives better results. Based on the connectivity of components, the components are counted as shown in figure 4. Figure 4: Component labeling shown as colored cells obtained from cleaned image 2.2 Malaria Identification As shown in figure 5 there exists a ring shape malaria parasite on two RBCs. These parasites are having shape like a ring and top of the ring is generally of blue color. By extracting the blue color from the image, the new' image formed will be consisting of ring shape malaria parasites only as shown in figure 5. 69

5 Figure 5 Shows the RBC with ring shape malaria and the extracted malaria parasite 2.3 Haemoglobin Abnormality The patient is said to be anemic when the HB (haemoglobin) of patient decreases below a certain level. The area inside RBC is called RBC core, if it is white inside it is called as paler area. If the paler area is more than 33% of total area then the HB of the patient has dropped down. Low HB has quite usual deficiency in human being. There is lot of reasons for deficiency of HB, one of the major reason is deficiency of Vitamin A. The major symptom of HB abnormality weakness and the skin, which is usually reddish inside skin, looks whitish. A specific algorithm has been devised for HB abnormality identification. Following are the steps followed to detect such abnormality. RBCs, which show the deficiency of HB, are first extracted for processing. The image also contains a WBC, which is blue in color and normally bigger in size as compared to RBC. Red color extraction will extract only RBCs. 3. Conclusion The detection of Malaria parasites is done by pathologists manually using microscopes. So, the chances of false detection due to human error are high, which in turn can result into fatal condition. This seminar curbs the human error while detecting the presence of malaria parasites in the blood sample by using image processing and automation. We achieved this goal using Image Segmentation smoothing processing techniques, gradient edge detection technique to detect malaria parasites in images acquired from Giemsa stained peripheral blood samples. The system in a robust manner so that it is unaffected by the exceptional conditions and achieved high percentages of sensitivity, specificity, positive prediction and negative prediction values. And the extraction of red blood cells achieves a reliable performance and the actual classification of infected cells. 70

6 References [1] F. B. Tek, A. G. Dempster, and I. Kale, Malaria parasite detection in peripheral blood images, in Proc. British Machine Vision Conference, Edinburgh, September [2] M-T Le, T. R. Bretschneider, C. Kuss, and P. R. Preiser. A novel semi-automatic image processing approach to determine plasmodium falciparum parasitemia in giemsa-stained thin blood smears, BMC Cell Biology, 9:15, [3] F. B. Tek, A. G. Dempster, and I. Kale, Computer vision for microscopy diagnosis of malaria, Malaria Journal 2009, 8:153, [4] F. B. Tek, A. G. Dempster, and I. Kale, Parasite detection and identification for automated thin blood film malaria diagnosis, Computer Vision and Image Understanding, vol. 114, pp , [5] S. S. Savkare, S. P. Narote Automatic Detection of Malaria Parasites for Estimating Parasitemia ICASSP

CLASSFICATION OF MALARIAL PARASITE AND ITS LIFE-CYCLE- STAGES IN BLOOD SMEAR

CLASSFICATION OF MALARIAL PARASITE AND ITS LIFE-CYCLE- STAGES IN BLOOD SMEAR 1 SRI HARTATI, 2 AGUS HARJOKO, 3 RIKA ROSNELLY, 4 IKA CANDRADEWI 1,2,3 Department of Computer Science and Electronics, Universitas