Accurate Microscopic Blood Cell Image Enhancement and Segmentation

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1 Accurate Microscopic Blood Cell Image Enhancement and Segmentation Syed Hamad Shirazi 1, ArifIqbal Umar 1, Nuhman Ul Haq 3, Saeeda Naz 1, Imran Razak 2 Hazara University Mansehra, Pakistan 1 Comsats Institute of IT, Abbottabad, Pakistan 2 King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia 3 razzakmu@ngha.med.sa Abstract. Erythrocytes (RBC) are the most common type of blood cell. These cells are responsible for the delivery of oxygen to body tissues. The abnormality in erythrocyte cell affects the physical properties of red cell. It may also decrease the life span of red blood cells which may lead to stroke, anemia and other fatal diseases. Until now, Manual techniques are in practiced for diagnosis of blood cell s diseases. However, this traditional method is tedious, time consuming and subject to sampling error. The accuracy of manual method depends on the expertise of the expert, while the accuracy of automated analyzer depends on the segmentation of objects in microscopic image of blood cell. Despite numerous efforts made for accurate blood cells image segmentation and cell counting in the literature. Still accurate segmentation is difficult due to the complexity of overlapping objects and shapes in microscopic images of blood cells. In this paper we have proposed a novel method for the segmentation of blood cells. We have used wiener filter along with curvelet transform for image enhancement and noise removal. The snake algorithm and Gram- Schmidt orthogonalization have applied for boundary detection and image segmentation, respectively. Keywords: RBC, SEM, Segmentation, Wiener filter, Curvelet 1 Introduction Computer based tools are becoming vital for the quantitative analysis of peripheral blood samples to help experts in diagnosis of blood cells disorder such as malaria, leukemia, cancer. The segmentation stage is initiated where red blood cells are separated from the background and other objects and cells present in the image. The success of these systems depends on the accuracy of segmentation. That is why different studies focus on the develop- adfa, p. 1, Springer-Verlag Berlin Heidelberg 2011

2 ment of accurate image segmentation algorithm. Although previous works give better results for segmentation of sparsely distributed normal red blood cells, only few of these techniques focus the segmentation of touching and overlapping cells. The first step of the blood cells image analysis is the image pre-processing where unwanted noise is removed from the image. For accurate segmentation image enhancement is vital. After the blood cells image enhancement, image segmentation process is initiated, in which individual cells are detached from each other and as well as from its background. The segmentation makes possible to extract features of the segmented cells and to discriminate among the normal and the infected blood cells. The traditional techniques for blood cell analysis are tedious and time consuming and also involve human intervention. The results of manual techniques are also dependent on the skills of an expert. The blood cell counting is a blood test that counts number of cells. The low or high cell counts as compare to given threshold helps the doctor or physician in diagnosing the disease related to blood cells. It is also called complete blood cell count, complete blood count (CBC), full blood exam (FBE) or full blood count (FBC). There are two methods in practice: Manual blood count Automated blood count Manual blood count: It is performed by trained physician who visually asses the blood smears by means of light microscope and in practice since 1950s [3]. This process of blood staining is time consuming and required more human effort. It is also require dedicated infrastructure including specialized instruments, dyes and well trained personnel. Automated blood count: Automated Image segmentation and pattern recognition techniques are used to analyze different cells by means of microscopic images. Quantifying these cells is very helpful in diagnosis and detection of various diseases like leukemia, cancer and many other fatal diseases. This method has been got attention of researcher in 1980 s *1+. The automated methods are more flexible and provide accurate results as compared to the orthodox manual methods. However, still image segmentation of

3 overlapping cells and complex shapes is challenging task for accurate blood cell analysis. A variety of segmentation algorithms are presented in the literature. When the erythrocytes cells are sparsely distributed and the overlapping objects do not exist then the segmentation is quite easy. Methods like thresholding [2],region growing, and edge detection are usually sufficient for delineating cell boundaries. The results achieved from these straight forward methods can also be improved by the use of mathematical morphology. However in the presence of overlapping objects the automation of the cell segmentation is challenging. The erythrocyte image segmentation is challenging because of several reasons i.e. these cells images having low contrast with variations in shapes. Sometimes the boundaries of cells are missing and vague which makes the segmentation more complex. In cell-based diagnosis, the hematologists or pathologist always make a decision based on the number of cell count, their distribution and their geometrical features. Abundant of computer vision methods are developed to extract useful information from medical images. Mostly due to the complexity of cells structure and overlapping between the cell boundaries and the background it is very challenging task to analyze it manually. The same problem is also faced during the automation of such systems. Because segmented images are used as input for computer aided systems for detection and diagnosis of disease. The precision of the results depend on the accurately segmented regions of the blood cells. The microscopic blood cell counting is still performed by the hematologists, which is essential for diagnostics of suspicious. The automation of this task is very crucial in order to improve the hematological procedures. The development of such systems not only accelerates the diagnosis process but it also improves the accuracy of detection of the disease. The segmentation phase requires more attention because it is vital for accurate feature extraction and classification. In microscopic blood cell images segmentation is a challenging task because of the complex nature of the cells and uncertainty in microscopic images. Image segmentation is mostly used as a pre-processing step to locate objects in SEM images. It is the process of

4 partitioning an image to objects, shapes and regions. To elaborate more, image segmentation not only focuses on the discrimination between objects and their background but also on the separation between different regions [4]. The image segmentation techniques can be classified as region based and contour based. For region based segmentation [5], morphological operators and watershed[3] approaches are used. The process of automated recognition of blood cells in microscopic images usually consists of four major steps include preprocessing, image segmentation, feature extraction and classification as shown in figure 1. In this paper our work is confined to pre-processing and image segmentation. 2 Related work In the literature, numerous state of the art techniques have been presented for image segmentation date back 1970s [6]. But unfortunately the studies on microscopic image segmentation are rare. Accurate segmentation is an essential for preserving the shape in order to detect the disease, which depends on the number of cells count and its type exists in the blood cell. The key tasks for blood cell analysis involve extracting all the targeted cells from the complicated background and then segmenting them into their morphological components, such as the nuclei and cytoplasm. Various types of cells are available in human blood including white blood cell (WBC), red blood cells (RBC), Platelets, transmigrated cells (tissue specific cells) and various combinations of these cells. The white blood cells (leukocytes) defend the body against diseases and viral attacks. The quantification of leukocytes is essential for the detection of disease. Granular (polymorphnuclear cells) and non-granular (mononuclear cells).the granulocytes contain three types of granules i.e. basophiles, nutrophils and eosinophiles, while the non-granular cells contain lymphocytes and monocytes [7]. The leukocyte cells have no color, by using the process of staining with chemicals leukocyte are made colorful to be visible under the microscope. During the staining process variation in color intensity is produced. Similarly during the acquisition of blood smear images from microscopes the quality of these images

5 may suffer from various types of illumination. These microscopic images may also be affected by camera lenses, exposure of time. To resolve the illumination issue image enhancement techniques are used as preprocessing. Several illumination and contrast enhancement techniques have been applied in literature. Local histogram equalization for contrast enhancement of parasite and RBC is used in [8]. The authors in [9] used adaptive histogram equalization for image enhancement [10-11]. In [12] the authors performed illumination correction using pre-defined illumination factor. Paraboloid modal for illumination correction is used in [13], to highlight the issues related to the features visibility under white light; polarized filters are used in light sources. While the issues related to color illumination still exist and require preprocessing. The most important phase in the blood cell analysis is segmentation. Blood cell segmentation and morphological analysis is a challenging problem due to both the complex cell nature uncertainty in microscopic videos. Blood cell segmentation yields a number of single cell images and each cell image is segmented into three regions, cell nucleus, cytoplasm and background. Huge amount of literature focused on the segmentation of white blood cells and differential counting. Edge detection is based on HIS (Hue Saturation Intensity) model is used for segmentation[14]. Color features selection and histogram based thresholding are used for segmentation blood cells cytoplasm and nucleus[15].non supervised nucleus region detection is performed in [16] before nucleus color segmentation using the G channel from RGB color coordinates. Blood cell segmentation algorithms can be divided into three categories: traditional segmentation, graph cut based segmentation, and active contour model. Traditional segmentation algorithms are based on water shed methods, thresholding and edge detection. Leukocyte segmentation is a difficult task because the cells are often overlaid with each other. In addition, the color and the intensity of an image often change due to instability of staining. Meanwhile, other factors also results in the difficulty of segmentation, such as: five classes of leukocyte and their different morphologies, light variation and noise. A new idea of leukocyte segmentation and classification in blood smear images is presented in [17]. They have utilized features related

6 to cytoplasm and nucleus. These features are color and shape of the nucleus. They have used SVM [18] for classification. In automated blood cells analysis the major challenge is segmentation for blood cell, because it affects highly the performances of the classification. Various methods have been presented in literature but still a lot of improvements are required. The major challenge to the microscopic image segmentation is the accurate segmentation of overlapping complex objects and shape variations present in image. In this paper our focus is on the blood cell image enhancement and image segmentation. 3 Methodology The blood automated blood cells analysis consists of image, acquisition, image preprocessing, image segmentation, classification and feature extraction. The scope of our work is confined to the image pre-processing and image segmentation in this paper. In figure1: the block diagram shows our proposed methodology for automated blood cells analysis. 3.1 Image Smoothening The blood smear images are widely acquired using bright field microscopy. The image quality is affected by the use of different illuminators like HBO, XBO and LED. Illumination variations degrade the efficiency of both the manual and automated system which may lead to biased analysis of blood smear. Therefore, image preprocessing is required to minimize these variations which will facilitate the image segmentation and it will also improve the accuracy of classification. Our method starts with the removal of unwanted particles or noise present in the image using wiener filter. The purpose of wiener filter is to reduce noise but preserves the edges. Wiener filter is statistical in nature as it adopts a least square (LS) approach for signal recovery in the presence of noise. We have to use the wiener filter which cannot be directly applied to

7 3D images. Therefore, separation of the RGB channels is required and then wiener filter is applied separately to each channel. The wiener filter measures the mean and variance of each pixel around. To get the finest detailed coefficients of noise free image, a Forward Discrete Curvelet Transform (FDCT) is applied to the input image. It is a multidimensional transform which can sense both the contours as well as curvy edges of the overlapping objects in the image. The FDCT has high directional sensitivity along with the capability to capture the singularities. Edge and singularity details are processed to extract the feature points. After obtaining the highest detailed coefficients Inverse Discrete Curvelet Transform is applied to high frequency band to obtain the detailed image. This detailed image is now having the stronger edges than the original and would perform better in lending edge details to the segmentation step. Pre Processing (Wiener Filter) Finest Co- Effiients (Curvlets) Segmentation (Gram-Schmidt orthogonalization) Results Classification Neural Network Feature Extraction (GLCM) Fig. 1. Proposed Solution 3.2 Image Segmentation: The image segmentation is the most crucial phase in hematological image analysis. Accurate image segmentation produces accurate results in subse-

8 quent stages. Our proposed image segmentation technique is based on Gram-Schmidt orthogonalization and snakes algorithm. For segmentation, we have used Gram-Schmidt process. This method is used in mathematics and above all in numerical analysis for orthogonizing a set of vectors in the inner product space. The Gram-Schmidt process each feature is considered as vector. The pixel intensities in RGB space are the elements of each vector. Fig. 2. Relation between w and v in 3D space The Gram-Schmidt method takes linearly independent set S={v1,v2,v3...Vn} and generate orthogonal set S={u1,u2,u3...un}. Where u and v denotes the vectors having inner product <u,v>... (1) Calculating the W k, in equation 1 by using Gram-Schmidt method, by applying it to a color image in order to intensify the required color v k. By applying this

9 process we get a composite image the region of interest with the required color have maximum intensity while the remaining have minimum color intensity. It requires proper thresholding which produce the desired segmentation. This process amplifies the desired color vector while reduce or weakening the undesired vectors. The snake algorithm is useful for the segmentation of objects where the edges are not well defined. The snake segmentation technique has a number of attractive characteristics that makes it useful for shape detection. The snake model can be parameterized contour defined within an image domain. The internal forces within the image domain control the bending characteristics of the line, while forces like image gradient act to push the snake toward image features. As the snakes coordinate vector can be defined as C(s)=(x(s),y(s)) so the total energy can be represented as. ( ) ( ( ) ( ( ))) (2) Where in equation 2Eext represent the image forces, while f represents image intensity and E int is the internal energy of the snake produced due to the bending or discontinuity. The E int inflict the snake to be small and smooth. Fig. 3. Step by step segmentation process

10 It avoids the wrong solution. The E ext is the external energy which is responsible for finding the objects and boundaries in the image. a ) Input Image b) Curvelet Finest Coefficients c) Enhanced Image Fig. 4. Image Enhancement a) Red Blood Cells b) Boundary detection Fig. 5. Erythrocyte Boundary detection and image segmentation In literature different methods are existing for the segmentation and classification of the white blood cells but these methods having limitations. Mostly

11 used method is thresholding approaches including region growing, watershed segmentation and otsu threshholding all of these methods suffers from inconsistencies especially where the images having considerable variations. Genetic algorithm is used for leukocyte segmentation in literature the main limitation of this method is that the presence of irrelevant and overlapping objects make difficult the possibility of accurate segmentation. Similarly methods like artificial neural networks (ANN), Support Vector machine (SVM), K-means, and Fuzzy c-means produce poor results for those images having complex background. Our method provides better results in the presence of complex background as well as the method produce better results for images having complex shapes and overlapping objects. 4 Conclusion It is concluded that accurate image segmentation is a crucial part for feature extraction and classification of blood cells. The accurate segmentation of blood cells is highly challenging in presence of complex and overlapping objects in microscopic images of blood cells. In this paper, we have proposed a hybrid segmentation technique which utilizes the capabilities of Gram- Schmidt orthogonalization method along with the snake algorithm to tackle inaccurate blood cell segmentation due to complex and overlap objects. The combination of these techniques leads to fruitful results. We have also exploited the Fast Discrete Curvelet Transforms (FDCT) Curvelet transform for image enhancement in order remove the noise and to obtain the finest coefficients which ensures the accurate image segmentation. In our future work will utilize these results for feature extraction and classification purpose. References 1. E. Meijering, Cell Segmentation: 50 Years Down the Road, vol. 29, no. 5, pp , S. S. Savkare and S. P. Narote, Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite s Life Stage, Procedia Technol., vol. 6, pp , 2012.

12 3. J. M. Sharif, M. F. Miswan, M. A. Ngadi, S. Hj, and M. Mahadi, Red Blood Cell Segmentation Using Masking and Watershed Algorithm : A Preliminary Study, no. February, pp , S.H. Shirazi, N.Haq, K.Hayat,S.Naz "Curvelet Based Offline Analysis of SEM Images"PLoS ONE 9(8): e103942, X. X. Li P, An unsupervised marker image generation method for watershed seg mentation of multiespectral imagery, Geoscience Journal,vol. 8(3), pp , J. Duncan, N.Ayache. Medical Image Analysis: Progress over two decades and thechallenges ahead, IEEE Transactions on Pattern Analysis and Machine Intelligence, Instituteof Electrical and Electronics Engineers (IEEE), 22 (1), , V.Kumar, A.K.Abbas, N.Fausto,J.Aster, Robbins andcotran Pathologic Basis of Dis ease. Saunders, Philadelphia, PA T.Pallavi. Suradkar, Detection of Malarial Parasite in Blood Using Image Processing, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 10, April Z.Karel, Contrast limited adaptive histogram equalization. Graphics Gems IV, (code: ), M. I. Khan, B.Acharya, B.K. Singh, J.Soni, Content Based Image Retrieval Approaches for Detection of Malarial Parasite in Blood Images, International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (2) : Ross, N.E., Pritchard, C.J.Rubin, D.M. and Duse, Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical & Biological Engineering & Computing, Vol44, (2006) 12. Tek, F.B., Dempster,, Kale, Parasite detection and identification for automated thin blood film malaria diagnosis Computer Vision and Image Understanding Vol 114 page (2010) 13. Ruberto C.D. Dempster A., Khan S. Jarra B., Analysis of Infected Blood Cell Images using Morphological Operators Image and Computer Vision Vol 20 (2002). 14. J.Angulo, G.Flandrin, Automated detection of working area of peripheral blood smears using mathematical morphology, Analytical Cellular Pathology, Vol 25, 37-49, M.Trivedi, J.C.Bezedek, Low-level segmentation of Zacrial images with fuzzy clustering, IEEE Trans. on System Man and Cybernetics, 16(4), B.ChulKo, J.W. Gim, J.Y. Nam, Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake, Micron , (2011). 17. Sabino, D.M.U., da Fontoura Costa, L., Gil Rizzatti, E., Antonio Zago, M. A texture approach to leukocyte recognition. Real-Time Imaging Vol 10,

13 18. D.Foran, P.Meer,Comaniciu. Image guided decision support system for pathology, machine vision and applications.machine Vision and Applications;11(4):213 24, 2000.