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A Method of Using Digital Image Processing for Edge Detection of Red Blood Cells [Sensors & Transducers (Canada)](Sensors & Transducers (Canada) Via Acquire Media NewsEdge) Abstract: Using a series of digital image processing methods, such as gray stretch, median filter, threshold segmentation, edge extraction and detection, detect the variations of red blood cells, realize the goal of identifying the shapes of variable red blood cells, and good results have been achieved. In conclusion, the average detection rate of abnormal red blood cells is above 80 %. This inspiring and conductive method is a tentative/experimental research which will play a good demonstration role in further application of image processing and detection in medical field. Copyright © 2013 IFSA. Keywords: Image processing, Morphological, Red blood cells, Detection. (ProQuest: ... denotes formulae omitted.) 1. Introduction With the development of information technology, image processing technology is becoming an essential and effective tool in scientific research. It is especially widely used and effective in the field of biomedical engineering. Besides CT technique of digital image processing, it is also widely used in medical diagnosis, such as chromosome analysis, cancer cell detection, etc [1-4]. According to geometric features obtained of the red blood cells, we can detect and research the pathological red blood cells. The method will play a good demonstration role for further application in the field of image processing technology in medicine. 2. Experimental Methods 2.1. Experimental Material The image samples of medical red blood cells (provided by people's hospital in Nanfeng County, Jiangxi Province). 2.2. Experimentation 2.2.1. The Grey Image Stretching of the Red Blood Cells While being a way of image linear transformation, the grey image stretching can greatly improve the visual effect for us. The gray level of all points in the image is transformed according to linear transformation function, which is one dimensional linear function [1], ... (1) For gray level transform equations: ... (2) The parameters fA is the slope of the linear function, fB is the y-axis intercept, DA shows the grayscale of the input image, and DB shows the grayscale of output image. While fA>\, the contrast of the output image will be increased; While fA<\, the contrast of the output image will be reduced; while/) =1 and f¿£0, the gray value of all the pixels will go up or down, and its effect is to make the image darker or brighter; If fA<0, dark areas will be brighten, and bright areas will be darken, complementary operations of the images are completed by the point operation. In a particular case, while fA=\, fß=0, the output image is the same as the input figure; While /)-1, fg=255, the grayscale of the input image and the output image is precisely reversed [5]. The Original Red Blood is shown in Fig. 1. The Enhanced Image by the Gray Stretch is shown in Fig. 2. 2.2.2. The Mean Filter of the Red Blood Cell Image Median filter of image is a kind of enhancement technique of image spatial domain filtering [1], which can reflect the texture characteristics of the spatial image, such as physical location, shape, size, and so on. The mean value of all pixels in the field is assigned to the output corresponding pixels so as to achieve the purpose of smoothing. 3x3 templates are adopted in this paper, and average filtering process is shown in Fig. 3. Fig. 3(a) shows a small part of an image, with a total of 9 pixels. Pi (i= 0, 1... 8) shows the grey value of pixels; Fig. 3(b) shows a 3x3 template, and Ki (i = 0, 1... 8) is called template coefficient; Odd numbers (such as 3x3, 5x5) are generally taken for the consideration of template size, and the median filter can be divided into the following steps: 1) MakeiG(r=0, 1... 8); 2) Make the template roam in the image, and make pixels of k0 and p0 overlap in Fig. 3. Gray value r0 can be calculated by the next type of output image which is corresponding to pixel p0 (as shown in Fig. 3(c); 3) All grey values of the pixels in the enhanced image can be obtained by calculating each pixel according to the type of Fig. 3(c). The process of the median filter can be applied to all the spatial filtering methods, that is to say, the function of the spatial filter is realized in the process of each pixel area through applying template convolution method. In order to remove noises, the image with a 3x3 templates has used the smooth processing operation. Results are shown in Fig. 4. 2.2.3. Threshold Segmentation of Red Blood Cell Image Threshold segmentation is a kind of regional segmentation technology [2], which can make the image gray level split into two or more gray intervals according to the user specified. Then using the differences in the gray level between extraction of target objects and the background, we choose an appropriate threshold value. By judging whether or not each pixel in the image meets the requirements of threshold value, we determine which area the pixels in the image belong to, the target area or background region. One of the commonly used threshold processing method is binarization processing of the image. Select a threshold then convert it to black and white binary image, which is pretreated by image segmentation and edge tracing, etc. Using the threshold value method of human-computer interaction and windows applications [6], we got the following red blood cells threshold segmentation image, see Fig. 5. 2.2.4. Image Edge Detection and Extraction Edge usually refers to the collection of those surrounding pixels which have a step change or roof change, and it is also an important characteristic on which image segmentation depends. The method of Laplace operator and Sobel operator are respectively used to sharpen the red blood cells [1, 7], and the following respective images can be got as in Fig. 6. 2.2.5. Red Blood Cell Image Processing 2.2.5.1. The Geometrical Characteristics of the Red Blood Cell Image Normal mature red blood cells are reddish or orange, with the shape of a disc, the characteristics of concentric undertint and pale center, the diameter of its light coloured area is about 1/3 of the diameter of the red blood cells. Red blood cell image samples chosen for test are shown in Fig. 7, the labeled cells are to be detected, which are random sampling of the red blood cells. First, the software interface of image as shown in Fig. 9 is processed by gray level stretch, median filter, threshold segmentation and prepared for the following extraction of the single red blood cells. After getting the red blood cell images with greater contrast which have been removed noises, the Windows XP system with a drawing software is used to extract the selected red blood cells images [6]. Number and arrange the selected red blood cells images, then a new arrangement of red blood cells images appears as shown in Fig. 10. 3. Results and Analysis Detect the edges of the red blood cell images according to the images as shown in Fig. 11, we get detection results of the first level (as shown in Fig. 12). In tests one, according to Fig. 12, we can see that red cells No. 15 and No. 17 are rectangular, not like a disc as normal red blood cells in medical science, therefore, we can conclude that the two red blood cells are abnormal. In tests two, through binarization process the single red blood cells are extracted, as shown in Fig. 13, and are prepared for the next calculation of geometrical characteristics of red blood cells. According to the binarization images in Fig. 13, observe the blood cells erythrocyte shallow areas, the cells No. 1, 3, 4, 5, 7, 8, 9, 11, 12, 13 can be observed with no shallow areas, or their light colored areas are smaller than 1/3 of the diameter of the red blood cells, so we can conclude that these red blood cells are abnormal. In tests three, respectively calculate the geometrical characteristics of the red blood cells after binarization processing in Fig. 14. Data aggregation of the red blood cell geometric characteristics is shown in Fig. 15. With the software used in this experiment, we get the result that the average area of the normal red blood cells is 830 or so, but average error range of red blood cells No. 20 and No. 23 is more than 100, so they can be regarded as abnormal red blood cells. Finally, the normal red blood cells detected are shown in Fig. 16. 4. Conclusions As we can see, the abnormal rate of the medical red blood cell image samples was 70 %, which was provided by the hospital in Fig. 8. However, the abnormal rate of red blood cells in the image we get in this experiment was 62.5 %. Therefore, we can basically conclude that the average detection rate of abnormal red blood cells in this study is more than 80 %. In short, through the image processing and detection process, using a variety of image processing technologies, we completed the extraction of single red blood cells, realized the detection of abnormal red blood cells, and achieved good results. But for red blood cell image detection there are still some problems to be solved: 1) Some of the discriminate error rates are still high, because only geometric features are used for analysis, while color, texture, the proportion of the internal structure factors were not considered; 2) Errors existing in the detection process certainly have some effect on the experimental results; 3) To facilitate testing and ensure higher detection rate, red blood cell images without overlapping are selected in this study, tests for overlapping cells will be explored with new treatment methods in the future. Acknowledgements We would like to thank Jiangxi Department of Education of Science and Technology Plan Projects (GJJ11490). 4. References [1] . Fu Desheng, Graphic image processing, Southeast University Press, Nanjing, 2001. [2] . Nie Bin, Medical image segmentation technology and its progress, Mount Taishan Medical School Journal, Vol. 23, No. 4,2002, p. 422-426. [3] . Tian Ya, Rao Nini, Pu Li Xin, The latest dynamic of domestic medical image processing technology, Journal of University of Electronic Science and Technology, Vol. 3, No. 2, 2001, pp. 3-9. [4] . Jia Minyi, Diagnostics, People's Medical Publishing House, Beijing, 1981. [5] . M. Christgan, K. A. Hiller, G. Schmalz et al., Accuracy of quantitative digital subtraction radiography for determining changes in calcium mass in mandibular bone, Journal of Periodontal Researches, Vol. 33, Issue 3, 1998, pp. 138-149. [6] . Cheng Wenbin, Jin Xiangfeng, Visual C++ utility, Beijing University of Aeronautics and Astronautics Press, Beijing, 1995. [7] . Xiao Yi, Long Mei, Ni I, Li Hongyang, Computer application in medical image processing, Medical Education and Technology of China, Vol. 15, No. 4, 2001, pp. 203-204. 1 Jinping LI,2 Hongshan MU,2 Wei XU 1 Software School, East China Institute of Technology, 330013, China Economic Development Zone Guanglan Avenue 418, Nanchang330013, China 2 Tel: 13699532208 2 E-mail: [email protected] Received: 22 July 2013 /Accepted: 25 October 2013 /Published: 30 November 2013 (c) 2013 International Frequency Sensor Association |