Morphology Based Automatic Disease Analysis Through Evaluation of Red Blood Cells

2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation Morphology Based Automatic Disease Analysis Through Evaluation of Red Blood Cells Sanjeevi Chandrasiri Department of Information Technology Sri Lanka Institute of Information Technology Colombo, Sri Lanka e-mail: sanji.c@sliit.lk Pradeepa Samarasinghe Department of Information Technology Sri Lanka Institute of Information Technology Colombo, Sri Lanka e-mail: pradeepa.s@sliit.lk Abstract Cell morphology has been an active area in the field of bio-medical research. In this paper, we introduce an automatic, cost effective and accurate way of red blood cell analysis and evaluation through Blob detection, Morphology operations and Hough circle transformation techniques for identification of four common types of anemia. Our research has filled the gaps in the existing literature by developing an integrated system to Count RBC, Diagnose Elliptocytes, Microcytic, Macrocyte and Spherocytes Anemia, Detect abnormalities and Separate overlapped cells, automatically, accurately and efficiently. The result shows an insight in the manually processed results with 99.545% accuracy of RBC count. Each sub method is closely running in the range 91%-97% of accuracy. The achievements are highlighted as efficiency through automation, cost effective, elimination of human error and easy to manipulate. Keywords- Morphology; Hemocytometer; Blob detection; Hough circle transformation; I. INTRODUCTION Blood related diseases are widespread and are considered as the root causes of much health related issues nowadays [1]. In the field of medical research it is known that various parameters such as the number of red blood cells, Hemoglobin level, Hematocrit level and Mean Volume Corpuscle are used for classifying diseases such as Anemia, Thalassaemia and Cancer [2]. In general, microscopic images are inspected visually and blood cells are counted manually by the Hematologist for measuring several parameters on diagnosis of diseases. But the process is time consuming and physically tiring for the Hematologists and it is also prone to errors due to emotional disturbance and human physical capability [1]. When compared with the manual process, though the automated analyzers give fast and reliable results regarding the number, average size, and variation in size of blood cells, they cannot reliably count the abnormal cells, overlapped cells and do not detect cell shapes [1]. In the literature, segmentation, extraction and estimation of red blood cells (RBC) have been addressed based on morphology approaches. J. Poomcokrak et. al [3] discussed a process of extraction of a single blood cell from sickle RBC and white blood cells from an image by consuming a neural network to search for RBC and count them using the morphological features of RBC. Their system has an accuracy level of 74% of automating the red blood count. N. Mahmood et. al [4] performed counting the number of red blood cells using a Hough transformation technique and produced close to 96% accuracy to manual counting. A major drawback of their system was the time taken by the user to determine the red blood cell parameters for drawing the Hough circles. An approach to detect normal and abnormal blood cells using morphological operations and neural network was presented by M. Veluchamy et. al [5]. The classification efficiency was declared as 80% and 66.6% for normal and abnormal RBC respectively. In [6] red blood cell classification was done using morphological image processing with several features relating to shape, internal central pallor configuration of red blood cells and their circularity and elongation was extracted and various types of RBCs were classified into several categories. All the studies above show that morphological approaches are utilized for segmentation, extraction and estimation of RBC but are not used to identify blood disorders and to evaluate the accuracy of the classification in recognizing the blood cell parameters associated with morphological features of RBC. For some of these approaches, the morphological operators, dilation, erosion, opening and closing have been used for better separation points of the objects, fill holes in the processed image and to eliminate the distortions. But none of the approaches mentioned in the literature have considered the use the morphological operators for separation of abnormal cells from the normal cells. In the classification of blood cells, some researches adopt geometric features such as Area, Perimeter, Roundness, Rectangle Factor and Elongation in their study. But none of them attempted to model an algorithm that combines the morphological features with compound blood cell parameters. In the research we carried out, in addition to filling the gaps mentioned above, we develop a software solution with an efficient algorithm to evaluate several types of disorders affected to RBC in a single unit of research with image processing techniques. To overcome the prevailing constraints in the process of identifying RBC and classifying diseases, a novel application 2166-0662/14 $31.00 2014 IEEE DOI 10.1109/ISMS.2014.60 318

is developed through research. The application designed and developed through research is able to 1) Perform an accurate and consistent blood cell count automatically 2) Generate blood count reports based on standard routine 3) Diagnose the following diseases automatically, efficiently and accurately Elliptocytes Hypochromic Microcytic (Iron Deficiency Anemia) Macrocyte Anemia Hereditary Spherocytes 4) Detect shapes of the abnormal red blood cells automatically 5) Separate overlapped cells from red blood cells In this paper, we detail out the application we developed through research. The outline of the paper is organized as follows. Section II details out the research methodology used in carrying out our research, Section III elaborates research findings related to Image processing of Blood Cell Images, results and evidence to evaluate the importance to the research aim. Section IV summarizes the results and the choice made in order to obtain a final outcome effectively and also discusses the further improvements with respect to the research that is carried out. We conclude this paper in the Section V. II. RESEARCH METHODOLOGY This work aims to apply image processing to extract and automatically count red blood cells from blood images taken from a digital blood smear microscope. As counting the exact number of decease cells in a given region of image will lead to early detection of the disease, we perform the RBC count in a series of steps as given in Fig. 1. A. Image Pre-processing Cellular bodies like Red blood cells have their own characteristics in shape and size. To uniquely identify cells various pre-processing techniques can be used on the basis of the input image [7]. The image is prepared by performing binary conversation with an estimated threshold value. This automatic value is generated using Otsu Thresholding [8] algorithm and the decision is taken based on the shape of the histogram. B. Red Blood Cell Identification To count the exact number of cells (normal or abnormal), Blob detection [7] technique is one of the fast and simple method that can be used where connected components are uniquely labeled based on a given heuristic. A variety of blob properties can be accessed to get information about the blob and to compute Statistics such as Area, Perimeter, Aspect ratio, Center of the mass, Average color etc [2]. All these can be used to classify blobs and decide if they hold objects that the application is interested in. The color scale image of the Red Blood Cell Microscopic Image Image Rendering and Labeling Compute Centroid Property Compute Area Property Blob Detection Remove Border Cells Count Red Blood Cells Figure 1. Overall Steps in Blob Detection Once the Blob detection process is finalized for the input image, the image is further processed to remove the cells or the objects that touch the image border. This verifies that accurate cells are remaining in the output and the red blood cells can be computed. In order to overcome the overlapped and irregular shaped cells the following equation is formed. C ( x, OIF = (1) A( x, where OIF is defined as Overlapped Irregular Factor, C (x, and R(x, represent pixels enclosed by cell boundary and bounded by the rectangle covering the cell respectively. In general we have calculated parameter values for the classification of blood cells using 20 samples and the equations and values are further justified by Shiraz E- Medical Journal paper titled New Approach to Red Blood Classification using Morphological Image Processing [6]. The maximum value of the sample set computed is 0.684, therefore if the OIF < 0.68 overlapped or irregular shaped 319

cells are being found otherwise regular cells are being counted. After the RBC counting process is finalized, the type of anemia is decided based on the further processing of the blood cell images. To achieve that, we have extracted several features as area, shape, center polar and rectangular factor of the RBC to classify them. After labeling using the blob detection, the area of the cell and the properties of the rectangle (area, height, width) were calculated for each red blood cell. The steps followed for identification and classification are given in Fig. 2. Process each Blood Cell SAF < 1.2 Yes No Elliptocyte Step 1 - Elliptocytes Identification: A new factor was calculated to differentiate between the oval and circular shapes of the cell. The factor was defined as Shape Area Factor (SAF). It is a proportion of larger length of the rectangular bounded cell to smaller length of the same cell as given in equation 2. Ll SAF = (2) L s where L l is the larger Length of Rect, L s is the smaller Length of Rect and Rect is referred as the bounded rectangle drawn closer to the edges of the blood cell. This factor helps in defining the circularity and elongation for each red blood cell. The circular cells were determined by taking the maximum value for circular cells as 1.20. If SAF > 1.20 the cells are considered to have elongation in shape and if SAF < 1.20 they are identified as in circular shape. Step 2 - Macrocyte Identification: In the next phase, diameter was lead with area of cell as a factor as Diameter Area Factor (DAF) for classifying circular shaped cells defined by equation 4. CD DAF = (3) C ( x, where C D is the Diameter of the Cell denoted by: DAF>0.024 No Macrocyte 4 C ( x, C D = C P (4) CP AP>0.30 Yes 0 Spherocyte 1 No Normal Microcyte Figure 2. Cell Identification and Classification where C P is the Perimeter which represent the sum of pixels of the line segment that passes through the center of the cell. By observing the calculated values, if the DAF < 0.024 the cell was classified as macrocytic cells. Step 3 - Hereditary Spherocytes Identification: Next the Center Polar (CP) of the cell was examined and for each cell that has a center polar this factor is 1, otherwise it is taken as 0. 1 if CP Detected CP = (5) 0 otherwise Step 4 - Hypochromic Microcytic Identification: Based on the importance of the CP of the cell then surveyed with Area Proportion (AP). This factor describes the separation of microcytic cells for normal cells. Microcytic cells have a big center polar, if AP < 0.30 then would be classified as Normal cell, otherwise the cell was classified as Microcytic cell. This is defined in the equation 6. P ( x, AP = (6) C ( x, where P(x, is the Center Polar Area which represents pixels enclosed by cell boundary of the center of the cell. 320

C. Red Blood Cell Disorder Identification and Classification Identifying types of anemic conditions through morphology was a crucial step and the input images followed preprocessing phases in order to be ready for detection. 1) Elliptocytes Identification and Classification: Elliptocytes also known as ovalocytosis is an inherited blood disorder in which an abnormally large number of the patient s red blood cells are elliptical rather than the typical biconcave disc shape [3]. This was determined by applying Hough Circle Transformation [4] to draw the selected circles using the same color as the background with the values from the equation 2. Figure 3. Elliptocytes Detection Process 2) Macrocyte Identification and Classification: Macrocyte is a class of Anemia in which Red blood cells are larger than their normal volume [9]. The large cells can be identified as referenced to the Fig. 2 and the other remaining cells can be removed using morphological erosion and dilation [10] with a computed reference diameter of a normal cell, Ref D = 43.10. fine tune the edges morphological operators erosion and dilation were used. Figure 5. Microcytic Detection Process 4) Hereditary Spherocytes Identification and Classification: Hereditary Spherocytes is a common disorder in which the body makes sphere-shaped red blood [11]. The detection of Sphere shaped cells in blood images are separated with the use of the CP factor referenced to the Fig. 2. By applying the absolute difference between the binary image and the interior gap filled image, can eliminate the unwanted background. This is defined as: c( x, = a( x, b( x, (9) where c(x, is the image after processing, a(x, is the binary image and b(x, is the interior gap filled image. Using the resulted image through subtraction, center polar of each cell can be extracted using the morphology operations erosion and dilation with much smaller structuring element [10]. Finally to isolate the sphere shaped cells Hough circle transformation [4] is applied. Figure 4. Macrocyte Detection Process Morphological operations are set of actions that process images based on shapes. Morphological techniques probe an image with a small shape or template called a structuring element [10]. In Morphology operations, the erosion of a binary image f by a structuring element s denoted by equation 7: f Θ s (7) The dilation of an image f by a structuring element s denoted by equation 8: f s (8) 3) Hypochromic Microcytic Identification and Classification: Hypochromic Microcytic also known as Iron Defi- ciency Anemia will show small than the normal, oval shaped cells with pale centers [4]. To Isolate the small cell from the input image CP and AP factors are used as referenced to Fig. 2 and to remove the remaining cells and Figure 6. Microcytic Detection Process III. DISCUSSION This section provides the results produced by the methods. Each method was executed using 10 sample images. The distribution of the subtypes did not impact the results because the images were converted with the Otsu [12] method into black and white images. A. Blood Disorder Detector Graphical User Interface The results are presented using a graphical user interface (GUI) which is developed in a user friendly way. The development environment is Microsoft.NET Framework 4.0 with Microsoft Visual Studio 2010 IDE. The images are 321

accessed and stored in Microsoft SQL Server 2008 and the Image Processing functions are done using OpenCV 2.1.0 with OpenCVSharp wrapper. Figure 7. Main Interface to estimate number of Red Blood Cells The system implemented can accept multiple images and process in order to obtain the count of blood cells. Decisions can be taken by the Hematologist based on the total count to verify the patient is anemic or not. Then further processing can be applied exclusively to determine the exact type of anemia. where N C, A I, D C and D F represents the Number of RBC cells, Input Image Area, Depth of the height of the counting chamber [1] and the Dilution factor respectively. Blood Reports can be generated for each patient that has come for blood test and as a reference the processed images can be saved for further analysis. It is essential to have a reporting module so that it will give the ability to process information faster and cost effectively. C. Evidence For estimating the number of red blood cells in blood smear image and the computational time taken to process each image a comparison is taken place between manual counting and estimation by computer by 10 images samples. From these image samples, the average computational time is 1.05 seconds per sample. The average accuracy of these 10 image samples are 99.545%. D. Results The Fig. 9 below shows the result of 40 tests applied on RBC images. Four separate methods addressed in previous section were tested in order to check the accuracy and the goodness of each. The above comparison can be graphically visualized in a scatter chart with Manual (Blue color) vs. Automatic (Red Color) analysis on RBC disorders. Figure 8. Overlapped Red Blood Cell Count For accurate RBC count, the overlapped cells are separated within the collection of cells as in Fig. 8. B. Reporting Generally blood count reports are generated in order to examine and verify the health status of the patient. Here represents the volume of the blood sample by counting the red blood cells per cumm through computing a formula. The volume will be differed from the magnification in x and y direction and the dilution factor that uses to minimize the overlapped cells. Volumeof Re d Blood Cells per cumm = NC (10) DF A D I C Figure 9. Data Analysis between Manual and Automatic approaches The TABLE I summarize the values on the Fig. 9 by computing the accuracy of detecting correct anemia disorder by comparing the results done through visual inspection. Table I ACCURACY ON SUB METHODS Elliptocytes Macrocytes Microcytics Spherocytes 97.86% 91.53% 93.13% 94.95% The results presented to detect anemia disorders automatically produce high fitness value for all of the four methods. The accuracy level of each method is closely running in the range of 91%- 97%. The results were promising leads to further research on different aspects of blood cells. Testing were done by taking 10 images for each method (total 40 images) as E1-E10, HM1-HM10, M1-M10 and S1-S10 denoting Elliptocytes, Microcytic, Macrocyte, Spherocytes Red Blood Disorder Test Image respectively. 322

IV. CONCLUSION The result shows a very good insight between the two approaches manual and the automated. The analytical step involved with 99.545% red blood cells were computed correctly and in detecting subtypes with Hough Circle and Morphology operations, in some cases the targeting circles were overlapped and the counting process of subtypes were not able to detect accurately, and averagely anemic cells were detected as following. The accuracy was 97% for the classification of Elliptocytes and with the Macrocyte Anemia accuracy decrease to 91% and for Hereditary Spherocytes and Hypochromic Microcytic the accuracy was closely equal. The achievements that followed through this study are highlighted as efficiency through automation, cost effective, elimination of human error and easy to manipulate. For further study could possibly use of analysis on infected cells that are distinct in texture and complex in structure in blood cells. Also would be able to predict diseases related to white blood cells based on the high and low counts. The research work can be further developed to recognize other biological cells like cancer cells. [10] G. Bradski and A. Kaebl, Learning OpenCV: Computer Vision with the OpenCV Library, 1st ed. United States of Ameria: O Reilly Media, 2008. [11] E. Montseny, P. Sobrevilla, and S. Romani, A fuzzy approach to white blood cells segmentation in color bone marrow images, IEEE International Conference, vol. 1, pp. 173 178, July 2004. [12] S. Rezatofighi, A. Roodaki, R. Zoroofi, R. Sharifian, and H. Soltanian-Zadeh, Automatic detection of red blood cells in hematological images using polar transformation and runlength matrix, ICSP Proceedings, pp. 806 809, October 2008. ACKNOWLEDGMENT The researchers would like to thank all who have helped in this study. Special gratitude goes to Dr. Chandima Kulathilake from the Department of Pathology, University of Sri Jayawardenapura, Sri Lanka for the assistance given to collect blood sample images. REFERENCES [1] M. R. Amin and S. Reza, Laboratory manual for practical Physiology and Biochemistry, 1st ed. Azimpur Dhaka, India: Medicogist Medical, 2005. [2] F. Kasmin, A. Prabuwono, and A. Abdullah, Detection of leukemia in human blood sample based on microscopic images: a study, Journal of Theoretical and Applied Information Technology, vol. 46, no. 2, December 2012. [3] J. Poomcokrak and C. Neatpisarnvanit, Red blood cells extraction and counting, The 3rd International Symposium on Biomedical Engineering, pp. 199 203, 2008. [4] N. Mahmood and M. Mansor, Red blood cells estimation using hough transform technique, Signal & Image Processing : An International Journal (SIPIJ), vol. 3, no. 2, April 2012. [5] M. Veluchamy, K. Perumal, and T. Ponuchamy, Feature extraction and classification of blood cells using artificial neural network, American Journal of Applied Sciences 9, pp. 615 619, 2012. [6] M. Taherisadr, M. Nasirzonouzi, B. Baradaran, and A. Mehdizade, New approach to red blood classification using morphological image processing, Shiraz E-Medical Journal, vol. 14, no. 1, pp. 44 53, January 2013. [7] M. Gupta, Cell identification by blob detection, UACEE International Journal of Advances in Electonics Engineering, vol. 2, pp. 56 59. [8] J. Zhang and J. Hu, Image segmentation based on 2d otsu method with histogram analysis, International Conference on Computer Science and Software Engineering, pp. 105 108, 2008. [9] R. Castleman and Z. Zhu, Digital Image Processing. Beijing: Publishing House of Electronics Industry, 1999. 323