ABSTRACT. 2. Platelates 3. White blood cells (WBC)

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1 Volume 119 No , ISSN: (on-line version) url: A FRAMEWORK FOR LEUKOCYTES SEGMENTATION USING MICROSCOPIC BLOOD SMEAR IN DIGITAL IMAGE PROCESSING WITH IOT 1 Chenigunta Sai Prabha 2 Gayathrii.M 3 Saranya.G 4 S.Leopauline 1, 2,3 UG Scholar Dept. of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College.Chennai TN India 4 Asst. Prof Dept. of Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College.Chennai TN India 1 cheniguntasaiprabha.1@gmail.com 2 gayanamasha1997@gmail.com 3 smartsaranya1997@gmail.com ABSTRACT Identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disorders, it can lead to classification of certain diseases related to blood. Traditional techniques for diagnosis of leukemia require a careful observation of peripheral blood and bone marrow samples under a microscope. This process is time consuming and depends on the skills and experience of an expert. Computerbased imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as leukemia.this project gives the count of leukocytes present in the blood smear using digital image processing(dip) in MATLAB process. The produced output is stored in mobile device with the help of cloud storage for easy analysis of the disease identification. KEYWORDS Leukocytes, MATLAB, cloud storage INTRODUCTION The blood is composed of three main components. They are: 1. Red blood cells (RBC) 2. Platelates 3. White blood cells (WBC) Red blood cell Red blood cells are in the blood in the form of tablets flattened in the middle and the circumference is partially bulged, have a bottom in the sides, one of the most important functions that they carry gases Components of red blood cells The red blood cells contain the hemoglobin that is responsible for supplying the human body with oxygen. The color of red and the reason for the presence of hemoglobin in them. Platelets Platelets are cycloidal substances or bodies that do not have a nucleus and formed in the bones, which are found in the blood. They are always broken when approaching the air, where the blood clots and breaks. Of its qualities that it does not have a specific form when they are in the blood, they are in the blood naturally and this has an important benefit as it does not cause bleeding in the blood any harm White blood cell The white blood cells are the most important components of the blood. they are responsible for defending the body against foreign 1657

2 bodies and cells that are exposed to it, cancer cells and destructive cells, so it has the ability to move from blood to tissues and to attack foreign objects by swallowing or producing antibodies. The main objective of this project is developing fully automated system for detection of leukemia form microscopic images. This system will differentiate between normal and abnormal cases (leukemia) using different combination of features. This algorithm concludes five main steps: Pre-processing step using image enhancement algorithm, image segmentation to identify the white blood cells, features extraction from segmented cells, feature Selection to select the most powerful features and finally classification stage in order to differentiate between normal and abnormal (leukemia) using different classifier EXISTING SYSTEM The process consists of the following stages: 1. Gathering the blood samples and applying image processing techniques 2. WBC cell segmentation 3. Feature Extraction 4. WBC classification The excisting system has the capacity to give blood count with all the composition in it. There is no specification while having the count of the leukocytes alone. It gives the composition only to a certain extent in the processing DRAWBACKS 1. They cannot have the accurate result. 2. This system needs a large storage and hence cannot be done in one system alone. 3. They don t have a mobile clouding facility. PROPOSED WORK Fig: segmented an classified WBC Patient to give Blood sample from Microscopic Screening to be done on smeared Analog Camera to be Fig:Blood smear image Input the picture to PC and using Apply Segmentat ion algorithm Sta Feat ure Digitiz ing the Sta ge 4 Use of Image segmentation Is an important stage of digital image processing, a process of segmenting the image into coherent and homogeneous areas according to a specific criterion such as color. The union of these areas must result in the reconfiguration of the original image 1658

3 The method used in segmentation is thresholding, thresholding is the simplest method of image segmentation, which replace each pixel in an image with black pixel if the image intensity is less than some fixed constant, or a white pixel if the image intensity is greater than that constant. The central premise when using a feature selection technique is that the data contains many features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant or irrelevant features are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated. They would be considered to obtained to have the considered results on the basis of feature extraction. Morphological Opening operator For enhancement we used the morphological Opening operator that remove small objects from the foreground pixels of an image. Post processing After pre- processing found that some of white blood cell that closed to other are connected, the watershed transform is applied to split the theconnected region. The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of the ridges. Feature extraction In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations Feature selection In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for four reason:- 1-simplification of models to make them easier to interpret by researchers/users. 2- shorter training times. 3- to avoid the curse of dimensionality 4- enhanced generalization by reducing over fitting (formally, reduction of variance). Working process: The segmentation is done in the following given way. Then obtain blood sample is taken. It is then inserted in the matlab test case for the processing.when the test case is run,then we get the obtain countoured,ostu s,morphological processing is obtained.then we get the image that is being extracted out of the unwanted segments nd pixels.the images we extract would be clearly obtained. It can be obtained with the changing of contrast and hue of the images. Fig:over all processing system 1659

4 After the processing is done we get the count of the leukocytes in their percentages that are available in the blood sample. They are then given such that all the five classifications are separately shown to have a clear idea about what type of disease is caused due to which granulate deficiency. The lymph nodes and spleen are only buffers of the white cells to be excreted in the body when needed. They also purify them of foreign bodies and dead cells before they are returned. Into the bloodstream and body tissues. Life span of white blood cells Output Fig:count of WBC classification This project is efficient in determining the count of leukocytes present in the blood smear, using microscopic images. The images are then clustered, gray scale processed and unwanted details are removed to obtain the clear count. The information is sent to the mobile devices using wireless network which is further used for the identification of diseases. REFERENCES 1. D KaviPriya, S R Krithiga, P Pavithra and J Rajesh Kumar, Detection Of Leukemia InBlood Microscopic Images Using Fuzzy Logic, National Conference On Recent Advances In Science, Engineering & Technologies, (2011) 2.Viswanathan Pa, Fuzzy C means Detection of Leukemia based on Morphological Contour Segmentation, Second International Symposium on Computer Vision and the Internet, (2011). 3.NiranjanChatap, SiniShibu, Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbor, IOSR Journal of Computer Engineering, Volume 12, Issue 5, Ver. III (Sep Oct. 2014), PP Yazan M. Alomari,1 SitiNorul Huda Sheikh Abdullah,1 Raja Zaharatul Azma,2 and Khairuddin Omar1 1 Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, UniversitiKebangsaan Malaysia, Bangi, Malaysia 2 Department of Pathology, UKM Medical Center, UniversitiKebangsaan Malaysia, Cheras, Kuala Lumpur, Malaysia(2014) 5.Bishop,Christopher(2006).pattern recognition and machine learning.berlin:springer.isbn The above figure shows the count of classifications in WBC. CONCLUSION 1660

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