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ScienceDirect Materials Today: Proceedings 3 (2016) 3361–3366
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International Conference on Advances in Bioprocess Engineering and Technology 2016 (ICABET 2016)
Assessment of Morphologically Altered RBCs using Image Processing Tools Sayari Ghosh, Arpan Roy, Debasish Sarkar* Department. of Chemical Enggineering, University of Calcutta, 92, APC Road, Kolkata 700009, India
Abstract Morphological study of Red Blood Cell is still reliant on microscopic imaging followed by manual counting, which is associated with personal bias, time and labour. In order to overcome these drawbacks, RBC morphology is quantified using image analysis tool. Because of being objective, reliable and reproducible, comparison of cell number between specimens is considerably more accurate and time saving (2 to 10 times lesser) with automatic counting than with manual counting. In this present work, normal and poikilocytic agent treated samples have been analyzed by microscopy and respective number of shape derivatives was calculated using standard image analysis protocols.
© 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016. Keywords: Red Blood Cell; morphology; microscopy; image analysis; automatic counting
1. Introduction Since the establishment of cell theory in early 19 th century, biologists are trying to explore relation between change of cell morphology and its effect on human health. Almost half a century ago, computers are used to analysis the cell. In the year 1950s, automatic classification of smears of exfoliated cells was developed with the ultimate aim to enable mass screening for cervical cancer. The application of thresholding-based decision rules were done by these systems to arrange one-dimensional (1D) microscopic line scans of a specimen [1]. Whereas in 1960s, two
* Corresponding author. Tel.: +91-9330806040; fax: +91-332-351-9755. E-mail address:
[email protected] 2214-7853© 2015 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advances in Bioprocess Engineering and Technology 2016.
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dimensional (2D) images was used for automated differential counting of White Blood Cells (WBC) according to their calorimetric and morphological measurements [2]. Today’s popular routine blood test is followed by the system developed in mid 1970s. These systems include multiple computer circuits to continue the tasks of analyzing the image of the previous cell, while grabbing the image of the present cell, and at the same time locating the next cell in the specimen simultaneously [3]. During these time, computer assisted microscopes were developed for tracing and morphological analysis of neuronal cells [4]. The conception of three-dimensional (3D) cell image analysis was established with the advent of confocal microscope systems in the 1980s. But until the 1990s, these images were not useful as computers were not powerful enough to handle 3D data, or even complex 2D data such as in histopathology [5].Over the past decades, cell image analysis methods have already become the basis of numerous studies involving cell counting (numbers), the identification of cell types or cell phases (shapes), the quantification of cell migration and interaction (morphodynamics), cellular sociology (tissue level organization), and intracellular structures (cell organization) [6].Till date, laboratory diagnosis of most of the diseases is being done by complete blood count (CBC) and peripheral blood smear (PBS) examinations. Recently, rapid acquisition and interpretation of data in microscopy has become a focus of biomedical research. Counting of RBC in a blood sample can give the pathologists valuable information regarding various haematological disorders. Therefore, various automatic image analysis methods have been developed to count the total number of Red Blood Cells (RBC) present in blood smear. There are four steps involved in estimating the RBC. These are acquisition, segmentation, feature extraction and estimating. The acquisition step utilizes the existing blood sample images. Next, the segmentation and feature extraction is done by using a morphological technique in order to distinguish the RBC from background and other cells. The background noises of an image are removed generally by using Otsu binarization method [7] followed by contrasting the threshold value of the images [8, 9]. Besides, Sauvolas and Niblacks algorithm can also be used for noise removal [10].The edge of cells can be detected either by canny edge detection [7] or by sobel edge detection operator [8].The last step is estimating the number of RBC is done by using Hough Transform [11]. However, detail morphological analysis of Red Blood Cell (RBC) carries immense importance in the diagnosis of many hematologic diseases e.g. anaemia, thalassemia etc. The evaluation of shape, size, inclusions and spikulations are included in this regard. Any alteration in RBC morphology has a pivotal role in the diagnosis of various diseases. Besides, the change of RBC morphology has a variety of application in neonatology, membranopathies, drug interaction studies etc. Under normal or healthy condition,RBC have biconcave disk like structure known as discocyte, but under pathological condition it can alter into stomatocytic, spherocytic or echinocytic population. However, the differential analysis of morphologically altered RBC population is still being carried out with common microscopic imaging followed by manual counting. Such methods are laborious for the pathologist and prone to personalized bias. Additionally, to identify the shape type of overlapping blood cells is also a major problem. Image analysis involves the conversion of features and objects in image data into quantitative information about these measured features and attributes. Therefore, to overcome the above drawbacks, in this study, an automatic image processing method has been proposed to accurately count the number fraction of normal and morphologically altered shape derivatives in any RBC population. 2. Materials and methods At first, blood is collected from healthy donor and treated with some poikilocytic agents of known concentration to get different RBC population having different shape derivatives. For this study, blood was suspended in 10 mM PBS (Phosphate buffer saline) or normal saline solution. After that, samples are ready for confocal microscopy in order to obtain the desired image. For this study, a 3×3 confocal stacked montage image was captured for each well by BD pathway 855 instrument using CCD camera. The proposed method is based on the standard confocal microscopic images of the normal or morphologically altered RBC samples, on which different image processing techniques were applied. Microscopy images in biology are often complex, noisy, artifact-laden and consequently require multiple image processing steps for the extraction of meaningful quantitative information. An outline of a general strategy for image analysis is illustrated in fig.1. Any cell in the population was identified after implementing suitable binary image generation, noise reduction and edge preservation. Therefore, total number of RBC cells is obtained. Different shape derivative of RBC cell can be estimated by comparing different cell properties obtained
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from the mentioned algorithm. The proposed method of estimating RBC shape derivative is implemented on MATLAB (R2014a 8.3.0.532) platform. Gray Image conversion to binary image Noise reduction Edge preservation/Enhancement
Morphological operation Total RBC count by labelling binary image RBC shape derivatives property extraction and estimation Fig.1. Illustration of the proposed method.
Fig.2. Raw image acquired from confocal microscopy.
2.1 Proposed counting method for RBC population Step1: An image of concern is read in the workspace as a matrix using ‘imread’ function. Step2: Image is converted into grayscale intensity image using ‘mat2gray’ function. Step3: The grayscale image is converted into binary image based on threshold using ‘im2bw’ function. The threshold value is calculated using ‘graythresh’ function. Step4: Opening-by-reconstruction (i.e. erosion followed by a morphological reconstruction) is performed on the thresholded binary image. Step5: Closing-by-reconstruction (i.e. dilation followed by a morphological reconstruction) is performed on the binary image which is undergone Opening-by-reconstruction procedure. Step6: The holes of the reconstructed image are filled with specific connectivity using ‘imfill’ function. Step7: The clearing of the image border is performed. Structures that are lighter than their surroundings and that are connected to the image border are suppressed. Step8: The image is again eroded twice with specific structure element. Step9: Label connected components in the image is calculated using ‘bwlabel’ function.
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Step 10: Different properties of image regions (shape measurements and pixel value measurements) are obtained using ‘regionprops’ function and stored as a table. Step 11: Fraction of shape derivatives are estimated according to the histogram of different properties obtained using ‘hist’ function and necessary loop algorithm implementation. 3. Results and discussion In this study, total fifteen samples (normal and poikilocytic agenttreated) were evaluated. The number of particular shape derivative obtained in a sample using this proposed technique were checked with manual counting by three different individuals. The automated counting output was observed to be close to manual counting as shown in Table 1. Besides, it also helps to estimate average size of the different type of RBC particles. The proposed image analysis method is based on the MATLAB programming. It is well known fact that, image processing and analysis can quantify objects and provides patterns in image data and capable to produce solution of several biological needs. It has two advantages over traditional manual methods of analysis: 1) it provides an unbiased approach to extract information from image data and test hypotheses, excluding the error occurs due to highly sensitive, human vision, which can be easily biased by pre-conceived notions of objects and concepts; 2) Once an image-analysis technique is devised, a huge number of microscopic images can be analysed in a very short time, facilitating the collection of large amounts of data for statistical analysis[12]. The process of image analysis starts with a digital image acquiring using a CCD camera but raw microscopy images obtained on digital CCD cameras are subject to various imperfections of the image acquisition setup, such as noise at low light levels, uneven illumination, defective pixels etc. It is often needed to first process the image for correction of such defects and also to enhance the contrast to accentuate features of interest in the image for subsequent analysis. To establish a perfect image analysis method, it is better to acquire good quality image. To maintain this features, one need to be very cautious while preparing the sample as well as performing the experiment. Thereafter, various image transformation and spatial filtering techniques has been applied. A variety of computational techniques is used to extract features and patterns from the images by enhancing the contrast in the images. To convert the uncompressed 16 bit depth raw image (captured in a CCD camera) into a binary image, ‘mat2gray’ command has been used. In general, biological images comprise of light objects over a constant dark background, in such a way that object and background pixels have gray levels grouped into two dominant modes. One obvious way to extract the objects from the background is to select a threshold that separates these modes. In this study, threshold value is adjusted by ‘graythresh’ function utilizing image histograms a reference, as it provide a means to visualize the distribution of grayscale intensity values in the entire image. They are also useful for estimating background values, determining thresholds, and for visualizing the effect of contrast adjustments on the image. The MATLAB function ‘graythresh’ calculates the threshold value by essentially maximizing the weighted distances between the global mean of the image histogram and the means of the background and foreground intensity pixels. It is observed that the thresholding operation segmented the cells quite well, but there are some overlapped cells as shown in Fig. 3(a) which will act as a one object while performing the counting algorithm. As a result, the proposed method can give incorrect or deviated results. In order to avoid this deviation, opening by reconstruction and closing by reconstruction at removing small blemishes without affecting the overall shapes of the objects as shown in Fig. 3(b) and 3(c). Consequently, the hole filled image is eroded twice so that overlapped cells get segmented in the image as shown in Fig. 3(f). ‘Erosion’ command shrinks or thins objects in a binary image. After that, all the RBCs in the figure are enumerated using the ‘bwlabel’ command, which provide both the label
Table 1.Comparative study between manual and automatic counting of Fig. 2 RBC shape derivatives
Manual counting
Automatic counting
Discocyte
107
104
Stomatocyte
307
303
Echinocyte
0
0
Spherocyte
3
2
Total
417
409
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Fig. 3. Represents (a) thresholded binary image; (b)image after opening by reconstruction; (c)image after closing by reconstruction; (d)filled image; (e) filled image with clear border; (f) eroded ROI filled image.
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(gives pixel values of each cells) and number of RBCs in the image. Quantitative information from these images has been derived by ‘regionprops’ function. Besides, the matrix representation of images in MATLAB also allows for easy management of data and calculation of quantities from microscopy images. It is observed that number of particular shape derivative is deviated from manual counting (2-4%). The reason of this deviation due to the designing of this proposed method without acquainting the cells present in the edges of image for counting. 4. Conclusion The present work is relevant for rapid, quantitative and qualitative approach for analysis of any cell population images. The proposed method is expected to reduce the involved man-hour compared to manual counting. Moreover, it is free from any personalized bias. It may also be noted that the proposed scheme is highly compatible with standard configurations and the corresponding software package is readily available in any personal computers. Acknowledgement The financial assistance provided by TEQIP, Phase II is gratefully acknowledged. References [1] W.E. Tolles,Transactions of the New York Acad. of Sci. 17 (1955) 250–256. [2] J.M.S. Prewitt, M.L. Mendelsohn, Annals of the New York Acad. of Sci. 128 (1966) 1035–1053. [3] K. Preston, Computer 9 (1976) 54–68. [4] E. Meijering, Cytometry Part A 77 (2010) 693–704. [5] M.N. Gurcan, L. Boucheron, A. Can, A. Madabhushi, N. Rajpoot, B. Yener, IEEE Reviews in Biomed. Engg. 2 (2009) 147–171. [6] J. Rittscher, Annual Review of Biomedical Engineering 12(2010) 315–344. [7] M. Habibzadeh, A. Krzyak, T. Fevens, A. Sadr, Proc. SPIE Med. Imaging 7963 (2011) 79633I-79633I-11. [8] A. Hamouda, A.Y. Khedr, R.A. Ramadan, Int. J of Comp Science 1 (2012) 13-16. [9] M.L. Feng, Y.P. Tan, IEEE Int. Conf. Mult. Expo. 1 (2004) 399-342. [10] J. He, Q.D.M. Do, A.C. Downton, J.H. Kim, Proc. of 8th ICDAR05 (2005) 538-542. [11] N.H. Mahmood, M.A. Mansor, Sig. Image Proc.: An Int. J. 3 (2) (2012) 53-64. [12] H.Y. Kueh, E. Marco, M. Springer, S. Sivaramakrishnan,Image analysis for biology: MBL Physiology Course, 2008, available at http://www.rpgroup.caltech.edu/courses/Physiology%20Matlab%202014/Papers%20for%20website/Image%20Analysis%20with%20Matla b.pdf.
