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Computerized analysis of erythrocyte aggregation from sequential video-microscopic images under gravitational sedimentation
ITBM-RBM 25 (2004) 67–74 www.elsevier.com/locate/rbmret
Original article
Computerized analysis of erythrocyte aggregation from sequential video-microscopic images under gravitational sedimentation Analyse automatique de l’agrégation érythrocytaire sous l’action de la pesanteur par imagerie vidéo-microscopique Sanjay Jayavanth, Megha Singh * Biomedical Engineering Division, Indian Institute of Technology, Madras 600036, India Received 16 December 2002; accepted 1 December 2003 Available online 18 February 2004
Abstract The erythrocytes form chain-like aggregates during sedimentation under gravitational field. The dynamic images of these are obtained by video-microscopic system by placing the erythrocyte suspension in plasma at hematocrit 5%, in a glass chamber of thickness 100 µm. The images at intervals of 2 min for 30 s duration are recorded. As the images are associated with high background noise, after digitization they are preprocessed for illumination correction, video de-interlacing, background subtraction, and deblurring, followed by post-processing involving edge enhancement, thresholding, median filtering and edge detection. By processing these images, to quantify the aggregation process, the morphometric parameters—projected aggregate area (PAA), projected aggregate perimeter (PAP) and form factor (FF), and sedimentation completion time (SCT) are obtained. To determine the variability of erythrocyte aggregation during human aging by this technique, blood samples from subjects of various age groups (from 20 to 60 years) are analyzed. The data obtained show the variability in the formation of aggregates in different age groups. The comparison in subjects of different age groups shows that the PAA, PAP and FF, and SCT are decreased significantly compared to that of subjects of age group 20–30 years. In subjects of 51–60 years, the formed aggregates are compact, which sediment faster compared to that of other age groups. © 2004 Elsevier SAS. All rights reserved. Résumé Les érythrocytes forment des chaînes d’ agrégats lorsqu’ils sédimentent sous l’action de la gravité. Des images dynamiques sont obtenues par un système vidéo-microscopique en plaçant la suspension d’érythrocyte dans un plasma à 5% d’hématocrite à l’intérieur d’une chambre de verre de 100 µm d’épaisseur. Les images sont enregistrées pendant30sec à 2 minutes d’intervalle. Après numérisation, les images fortement bruitées sont traitées pour les corriger de leurs défauts de luminance, d’entrelacement vidéo et de bruit de fond. Un post-traitement est appliqué pour rehausser et détecter les contours. Pour mesurer le processus d’agrégation, des paramètres morphométriques sont étudiés: aire projetée de l’agrégat (PAA), périmètre projeté de l’agrégat (PAP), facteur de forme (FF), temps de sédimentation (SCT). Pour déterminer la variabilité de l’agrégation érythrocytaire avec le vieillissement, des échantillons de sang provenant de sujets d’âge divers (20 à 60 ans) sont analysés. L’analyse montre que les PAA, les PAP et les FF, et les SCT diminuent avec l’age surtout chez les sujets de 51-60 ans pour lesquels les agrégats formés sont compacts et sédimentent plus rapidement. © 2004 Elsevier SAS. All rights reserved. Keywords: Human aging; Erythrocyte aggregation; Sedimentation; Dynamic imaging; Morphometric parameters Mots clés : Vieillissement ; Agrégation érythrocytaire ; Sédimentation ; Image dynamique ; Paramètres morphométriques
* Corresponding author. E-mail address: [email protected] (M. Singh). © 2004 Elsevier SAS. All rights reserved. doi:10.1016/j.rbmret.2003.12.002
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1. Introduction Erythrocyte aggregation is an important phenomenon occurring in the blood circulation at micro-circulatory level. Aggregates are formed due to adsorption of plasma proteins such as fibrinogen and globulin or macromolecules (dextrans) on erythrocyte membrane, leading to the formation of three-dimensional chain-like structures. These are formed under low flow or stasis condition and are completely disaggregated into individual cells under rapid flow conditions [1]. The alterations of erythrocyte aggregation in diseased conditions could be detrimental to blood circulation, causing increased risk of cardiovascular and cerebrovascular disorders [2]. Hence, precise monitoring of this process is of clinical importance for better understanding of the pathophysiology of the diseases and to supplement the diagnostic parameters [3]. For indirect quantification of erythrocyte aggregation, the erythrocyte sedimentation rate (ESR) is occasionally used [1]. But it is a time consuming method and does not provide the precise details of the aggregation process. Microscopic technique, based on direct observation of the aggregates under stasis conditions, is represented in terms of number of aggregates per unit volume [1]. Increase in the apparent viscosity of blood at low shear rate, as determined by viscometry, is partly attributed to the erythrocyte aggregation [4]. These aggregates are also visualized by rheoscopic method while measuring the blood viscosity at various shear rates [4,5]. These measurements are carried out either under stasis conditions or by shearing at high shear rate followed by bringing the sample to near stasis conditions. The laser aggregometry techniques employ continuous recording of backscattered or transmitted light signal from the suspension of erythrocytes [6–8]. Similar to laser scattering signals the erythrocyte aggregation process is further analyzed by processing of the ultrasound backscattered signals [9–10]. By applying the pattern recognition procedures on backscattered ultrasound Doppler signals the size of the aggregate is determined [11]. The quantification of the aggregation process under dynamic conditions is further carried out in terms of various aggregation parameters as determined by the changes in the transmitted light intensity [12]. Despite the versatility of the latter technique the data on morphological changes, required to provide the details of the cellular bonding and shape alteration, could not be obtained. Maeda et al. [13] obtained the rheoscopic images under stasis or low shear rate conditions and quantified the growth rate of aggregates. Chen et al. [14] generated the flow of erythrocyte suspension in a glass chamber under low flow conditions to analyze the variation in size and shape of aggregates. The formation of three-dimensional aggregate structure requires very low shear conditions, similar to micro-circulation. Such a dynamic condition could be achieved during the gravitational sedimentation of the aggregates at an appropriate sample hematocrit, and the formation and movement of these could be monitored and recorded by a video-microscopic system.
In contrast to conventional techniques the images obtained by this procedure are associated with high background noise, which require sophisticated image processing procedures in order to obtain the desired information. Therefore, the present work is aimed to develop a computerized system for dynamic analysis of aggregation of erythrocytes from their sequential images obtained under gravitational sedimentation conditions. During human aging the blood constituents are subjected to various changes, which may affect the formation and other characteristics of the aggregates. By application of this technique the changes in aggregation process are further analyzed. 2. Materials and methods 2.1. Experimental technique Fig. 1 shows the block diagram of the aggregates imaging system. To record data the microscope (Leitz Dilux22, Germany) in transmission mode, with NPL Fluotar objective of magnification 40× (phase contrast), was used. A wellcollimated beam of light from a 12 V 100 W tungsten halogen lamp, by using a field diaphragm and by incorporating an interchangeable condenser, was achieved. This combination was ideal for recording of aggregates in the sample chamber, which was vertically mounted on the microscope stage, achieved by placing the microscope horizontally on a specially designed supporting stand. The dimensions of sample chamber, made of optically flat glass slide, were 45 × 13 × 0.1 mm. The gap between vertical plates of 100 µm allowed the free movement of erythrocytes and their formed aggregates. For recording of images in a video cassette by a VHS video cassette recorder (National NV-370, Japan) at a rate 25 fps the microscope was fitted with color video camera system (VK 4003, Philips Newvicon, The Netherlands). These images were continuously monitored by displaying on a monitor (Philips Match-line Video RGB Monitor, The Netherlands). 2.2. Sample preparation Healthy subjects, without any clinical disease and normal plasma and serum biochemical levels, of age ranging from 20 to 60 years, were selected. These were divided into four groups: 20–30, 31–40, 41–50 and 51–60 years in Groups I–IV, respectively. Fresh blood sample from each subject was collected by venepuncture in a test tube containing citrate phosphate dextrose (10:1.4), as an anticoagulant. Each sample was centrifuged at 3000 g for 20 min. Thereafter, the supernatant plasma was separated and the buffy layer on top of the cells was gently removed and discarded. A suspension of 5% hematocrit was prepared in the plasma for imaging of aggregation process of erythrocytes. 2.3. Data acquisition A well-mixed erythrocyte suspension was placed in the glass chamber and was vertically mounted on microscope
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Fig. 1. Schematic of (a) Aggregation imaging system, and (b) sample chamber.
stage. The field of view was adjusted at the center both with respect to the height and sidewalls of the chamber. The sequential images of aggregates and individual erythrocytes during sedimentation were recorded on a video cassette recorder for a duration of 30-s after every 2.0 min interval till the end of process, at room temperature 25 ± 1 °C. These data after digitization with Matrox frame capture card (Matrox Marvel G400-TV) were stored in the computer for further processing. 2.4. Data analysis The flow chart of image processing and analysis procedures is shown in Fig. 2. Image processing was carried out using Matlab software package for a given sequence of images that were recorded under identical conditions. The initialization of the program involved setting of image counter, number of images to be processed, type of image, the path, source and destination folder names from where the images were to be read and stored. Each image from the sequence was loaded and processed to get a region of interest (ROI) mask and its corresponding image parameters, which were stored in the destination folder at the end of the analysis. With the processing of each image, the image counter was incremented and then compared with the number of images to check if all the images were processed. The same routine was repeated with the next image. The preprocessing steps involved illumination correction, video de-interlacing, background subtraction, and deblur-
ring. Usually the images after digitization had poor or nonuniform illumination, which was corrected by enhancing the image using histogram equalization [15]. The digitized images were further associated with odd fields, which look like vanishing blind strips, and noise. These effects from the images were removed by video de-interlacing, by using a rotational symmetry Gaussian filter [15]. This operation was followed by subtraction of the background from this image, which was recorded separately under exactly identical conditions. The minimization of image degradation due to motion blur, using Lucy–Richard blind deblurring algorithm, was carried out [16]. The restored image was found to be satisfactory after 10 iterations. The post-processing of the image involved edge enhancement, thresholding, median filtering and edge detection. The resulting image after preprocessing was first edge enhanced and thresholded by Otsu’s method [17], by using a global gray level threshold followed by median filtering. The edge detection of the filtered image was carried out by Canny’s edge detection scheme [18]. A ROI analysis using morphological reconstruction method [19] to process the edgedetected image was further used. For this purpose, an ROI mask was developed which exactly matched the aggregate and cellular areas in the original image. The ROI of this image was filled with white pixels. The filled image that matches the ROI of the original image was further labeled [20] and was used for determining the aggregate parameters. The resulting data were stored in a data file.
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of all the objects. The ‘Pi’, the perimeter of the ith object, in terms of number of pixel was equal to the sum of all the pixels that belonged to this object, i.e., Pi = 兺 f(x, y), x, y { i (4) Size distribution of aggregates (SDA): To obtain the size and spatial distribution of all aggregates, based on above data, the projected aggregate size with respect to aggregates number was plotted. (5) Sedimentation completion time (SCT): Time duration between the first frame and the last frame with the PAA of the sedimenting aggregates approaching to the desired minimum size of area (10,000 ± 1000) pixels. (6) Form factor (FF): Calculated as (PPA)2/4p(PAA). 3. Results
Fig. 2. Sequence of image processing procedures.
2.5. Analysis of aggregate parameters From the processed images following parameters were calculated: (1) Number of aggregates: The number of labeled objects in the image corresponded to the number of aggregates and single cells. The counting of objects was carried out by a row-wise scan based on neighborhood connectivity [15]. (2) Projected area of aggregate (PAA): The area of each aggregate/individual cell was the number of pixels in that labeled object. The PAA of a given image was given by the sum of the areas of all the objects. Thus ‘Ai’ the area was equal to the sum of all the pixels that belong to ith object [21] Ai = 兺 f(x, y),x, y { i (3) Projected perimeter of aggregate (PPA): For this purpose, the contour of the ROI-filled image was first extracted and was labeled in the same manner as above. The perimeter of each aggregate/individual cell was the number of the pixels on the contour of that labeled object. The PPA was the sum of the perimeters
Fig. 3 shows the images after implementation of various steps of image processing. The original image (a) is after the correction of illumination and de-interlacing. The background subtraction is carried out in gray mode. The constant noisy background that can be seen in original image is effectively removed from this image (b). By deblurring the image, edges of the cells and aggregates are well observed (c). The thresholded image shows that the aggregates are clearly delineated from the rest of the image (d). The edge of the image shows many discontinuities, which depend on the image quality and the threshold chosen for edge detection (e). In order to get continuous edge, morphological contour structuring is employed wherein the image edge is subjected to a deliberate over-dilation by a known size of line-structuring element. In the dilated image (f) the discontinuities in the edge are effectively removed, but the ROI is blown up because of dilation. In order to recover the original ROI, the dilated image is eroded by the same size of the linestructuring element, employed for dilation. The eroded image shows that the edge is continuous and the original ROI is restored (g). The ROI image is further filled with white pixels for the PAA determination (h). Thus by the present contour restructuring the automation of the processing of whole sequence of images, without the necessity of interactive thresholding and edge detection, could be possible. To establish that the ROI is successfully extracted from the original image, the final image (h) is inverted and is overlapped on the deblurred image (c) to get the masked image (i), which shows close match of the area of interest as in the original image. Fig. 4 shows the examples of the ROI images of aggregates and their size distribution in subjects of different age groups after an interval of 16 min. From these, it is evident that the pattern of aggregate images and their size distribution depend on the age of the subject. Large number of aggregates of varying sizes are observed in subject of age 20 year, whereas, this is the minimum in subject of age 53 year. Even at constant hematocrit, the formed aggregates of elderly subject sediment faster, indicating that these are large size but compact aggregates.
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Fig. 3. Sequence of processed images. (a) Original image after illumination, correction and de-interlacing, (b) background subtracted image with edge enhancement, (c) deblurred image, (d) thresholded image, (e) edge image, (f) dilated image, (g) eroded image, (h) ROI image, (i) mask of ROI and deblurred image.
From the processed images of the aggregates, the PAAs of various samples are calculated. Fig. 5 shows the variation in the mean PAA of samples of different age groups, as determined sequentially throughout the sedimentation process. Initially the sedimenting aggregates are of larger size but with progression of time their size is reduced. The SCT is the maximum for age Group I and the minimum for subjects of Group IV. The change in the PPA represents the change in its contour. Fig. 6 shows the variation in the PPA of aggregates in various age groups. The FF of aggregates, which determines the compactness, indicates that a loosely bound aggregate correspond to high PPA, whereas, a compact one correspond to its low value. The variation of the SCT, PAA, PPA and FF are given in Table 1. These parameters complement each other and vary significantly in Group IV compared to Group I. In Group III, the change is marginally significant and not significant for Group II. The shorter SCT is associated with lesser PPA and PAA. The FF is the minimum for Group IV and is the maximum in Group I.
Table 1 Comparison of sedimentation completion time (SCT), projected area of aggregate (PAA), and projected perimeter of aggregate (PPA), and form factor (FF) different age group subjects with control group (20–30 years). Number of subjects in each group = 5 Age group (years) 20–30 31–40 41–50 51–60 a
SCT a (min)
PAA (pixels)
PPA (pixels)
FF
5230 ± 529 4940 ± 481 3800 ± 345 * 3480 ± 256 **
57.26 ± 5.1 52.85 ± 5.4 43.21 ± 4.1 * 40.74 ± 5.1 **
After 16 min #
38 ± 3 36 ± 3 28 ± 4 * 26 ± 4 **
38030 ± 4343 36757 ± 3412 26601 ± 2312 * 23663 ± 1234 **
Mean ± S.D., * P = 0.1, ** P = 0.05, # = control
4. Discussion The present study involves the development of this technique for recording and processing of the sequence of images throughout the sedimentation process, and to compute the shape-related parameters. The image analysis is automated for a set of images, which are recorded and processed under
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Fig. 4. The ROI images and aggregate size distribution of subjects of different age groups at t = 16 min.
identical conditions. The contour restructuring technique employed in the image processing makes the automation of image analysis possible for the entire sequence of images without the necessity of interactive thresholding and edge detection. As and when required, the process can be interrupted. The measurement technique, unlike other methods, involves two aspects—visualization and quantification in terms of image and shape-related parameters under dynamic conditions. The variation of the projected area and perimeter of the aggregates and their FF quantifies this process. This technique through imaging and calculation of its related parameters elaborates the roles of various plasma and cellular parameters. One of the major factors in enhancing the erythrocyte aggregation is plasma fibrinogen, which is also responsible in enhancing erythrocyte aggregation in elderly subjects [1,22–28], which quantify the change in terms of the PAA and PPA. The change in the FF is generally
applied to calculate the deviation from the discoidal shape of an individual erythrocyte. This aspect has recently been applied to relate the cholesterol-enrichment in erythrocyte membranes [29]. But in aggregation process the aggregates do not show a well-defined shape. Hence the FF could not be used to show the marked deviation from a normal discoidal erythrocyte shape. The human aging is a biological process. There are several changes in the system associated with this process. To show the variation due to this process the blood samples are obtained from subjects of various age groups. The aggregation of erythrocytes, as determined by conventional methods, also shows an increasing pattern in subjects of age group 50– 65 compared to subjects of younger group (between 20 and 30 year) [29,30]. Our present findings based on shape descriptors are not only in agreement with these but also with our results obtained by laser light aggregometer [31]. The
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technique can be implemented in routine physical and clinical analysis, erythrocyte–drug interaction and monitoring of patients’ progress after drug therapy.
References [1]
[2] [3] [4]
[5]
[6] [7] [8]
[9]
[10]
[11] Fig. 5. Mean projected area of aggregates of subjects of different age groups. [12]
[13]
[14]
[15] [16] Fig. 6. Mean projected perimeter of aggregates of subjects of different age groups.
[17]
enhanced aggregation in aging population is primarily due to the increased level of the plasma proteins [30]. Such changes in the elderly may lead to greater risk of cardiovascular and cerebrovascular diseases [32].
[19]
In conclusion, this technique is simple and of high reproducibility, which provides the visual and quantitative details of the formed erythrocyte aggregates. With these features this
[18]
[20] [21] [22]
Chien S. Biophysical behavior of red cells in suspension. In: Surgenor DM, editor. The Red Blood Cell, 2. New York: Academic Press; 1975. p. 1032–133. Muller R. Hemorheology and peripheral vascular diseases: a new therapeutic approach. J Med 1981;12:209–36. Puniyani R. Clinical Hemorheology: New Horizons. New Delhi: New Age International; 1996. Schmid-Schoenbein H, Wells R, Schildkraut R. Microscopic and viscometry of blood flowing under uniform shear rate (rheoscope). J Appl Physiol 1969;26:674–8. Schmid-Schoenbein H, Malotta H, Streisow F. Erythrocyte aggregation, causes, consequences and methods of assessment. Tijdschr NVKC 1990;15:619–25. Gaspar RA, Thurston GB. Erythrocyte aggregate rheology by transmitted and reflected light. Biorheology 1988;25:471–87. Stoltz JF, Paulus F, Donner M. Experimental approaches to erythrocyte aggregation. Clin Hemorheol 1987;7:109–18. Hardeman MR, Goedhart PT, Dobbe JGG, Lettings KP. Laser-assisted optical rotational cell analyzer: a new instrument for measurement of various structural hemorheological parameters. Clin Hemorheol 1994;14:605–18. Swarnamani S, Singh M. Analysis of erythrocyte aggregation mechanism in presence of dextrans and magnetic field by ultrasound scattering in blood. Biorheology 1989;26:847–62. Boynard M, Lelievre JC. Size determination of red cells aggregates induced by dextran using ultrasound backscattering phenomenon. Biorheology 1990;27:39–46. Karabetsos E, Papaodysseus C, Kautsouris D. A new method for measuring red blood cell aggregation using pattern recognition techniques on backscattered ultrasound Doppler signals. Clin Hemorheol Microcir 1999;20:63–75. Singh M, Kumaravel M. A computerized system for sequential analysis of aggregation of erythrocyte under dynamic conditions. Comput Biomed Res 1994;27:325–36. Shiga T, Maeda N. Kinetic study on aggregation of red blood cells by successive analysis of moving video images. In: Stoltz JF, Donner M, Copley AL, editors. Hemorheologie et Agregation Erythrocytaire, 3. Paris: E. M. Inter.; 1991. p. 38–45. Chen S, Barshtein G, Gavish B, MahlerY,Yedgar S. Monitoring of red blood cell aggregability in a flow chamber by computerized image analysis. Clin Hemorheol 1994;14:497–508. Gonzalez RC, Wood RE. Digital Image Processing. New York: Addison-Wesley; 1999. Lucy LB. An iterative technique for the rectification of observed images. Astronom J 1974;79:8–37. Otsu NA. Threshold selection method from gray-level histograms, IEEE Trans. System Man Cybernet 1979;9:62–6. Canny J. A computational approach to edge detection, IEEE Trans Pattern Analy. Machine Intell 1986;8:679–98. Soille P. Morphological Image Analysis: Principles and Applications. Berlin: Springer-Verlag; 1999. Haralick RM, Shapiro LG. Computer and Robot Vision, I. New York: Addison-Wesley; 1992. Rosenfeld A. Iterative methods in image analysis. Pattern Recogn 1978;10:1811–70. Sanjay J, Singh M. Computerized descriptors for analysis of hypercholestromic cells. Proc Intern Conf Biomed Eng Chennai 2001:291–3.
74
S. Jayavanth, M. Singh / ITBM-RBM 25 (2004) 67–74
[23] Skalak R. Aggregation and disaggregation of red blood cells. Biorheology 1984;23:463–76. [24] Tarallo P, Henry J, Gueguen R, Siest G. Reference limits for plasma fibrinogen. Europ J Clin Chem Clin Biochem 1992;30:745–51. [25] Barasch E, Benderly M, Graff E, Behar S, Reicher-Reiss H, Caspi A, Pelled B, Reisin L, Rouguin N, Goldbourt U. Plasma fibrinogen level and their correlate in 6457 coronary heart disease patients, The Bezafibrate Infraction Prevention study. J Clin Epidemiol 1995;48: 757–65. [26] Stoltz JF, Singh M, Riha P. Hemorheology in Practice. Amsterdam: IOB Press; 1999. [27] Avellone G, Garbo D, Panno AV, Cordova R, Alletto G, Raneli G, De Simone R, Strano A, Bompani GD. Hemorheological components in pre-geriatric and geriatric age range in a randomly selected western Sicily population sample (Casteldaccia Study). Clin Hemorheol 1993; 13:83–92.
[28] Mchedlishvili G. Disturbed blood flow structuring as critical factor of hemorheological disorders in microcirculation. Clin Hemorheol Microcir 1998;19:315–25. [29] Manjunatha M, Singh M. Digital analysis of induced erythrocyte changes in hypercholesterolemia under in vitro conditions. Curr Sci 2000;79:1588–91. [30] Ajmani RS, Rifkind JM. Hemorheological changes during human aging. Gerontology 1998;44:111–20. [31] Sanjay J, Singh M. Changes in erythrocyte aggregation with human aging. Curr Sci 2002;82:191–6. [32] Banerjee AK, Pearson J, Gilliland EL, Goss D, Luwis JD, Striling Y, Maeda TW. A six year prospective study of fibrinogen and other risk factors associated with the mortality in stable claudicents. Thrombo Haemost 1992;68:261–3.
Original article
Computerized analysis of erythrocyte aggregation from sequential video-microscopic images under gravitational sedimentation Analyse automatique de l’agrégation érythrocytaire sous l’action de la pesanteur par imagerie vidéo-microscopique Sanjay Jayavanth, Megha Singh * Biomedical Engineering Division, Indian Institute of Technology, Madras 600036, India Received 16 December 2002; accepted 1 December 2003 Available online 18 February 2004
Abstract The erythrocytes form chain-like aggregates during sedimentation under gravitational field. The dynamic images of these are obtained by video-microscopic system by placing the erythrocyte suspension in plasma at hematocrit 5%, in a glass chamber of thickness 100 µm. The images at intervals of 2 min for 30 s duration are recorded. As the images are associated with high background noise, after digitization they are preprocessed for illumination correction, video de-interlacing, background subtraction, and deblurring, followed by post-processing involving edge enhancement, thresholding, median filtering and edge detection. By processing these images, to quantify the aggregation process, the morphometric parameters—projected aggregate area (PAA), projected aggregate perimeter (PAP) and form factor (FF), and sedimentation completion time (SCT) are obtained. To determine the variability of erythrocyte aggregation during human aging by this technique, blood samples from subjects of various age groups (from 20 to 60 years) are analyzed. The data obtained show the variability in the formation of aggregates in different age groups. The comparison in subjects of different age groups shows that the PAA, PAP and FF, and SCT are decreased significantly compared to that of subjects of age group 20–30 years. In subjects of 51–60 years, the formed aggregates are compact, which sediment faster compared to that of other age groups. © 2004 Elsevier SAS. All rights reserved. Résumé Les érythrocytes forment des chaînes d’ agrégats lorsqu’ils sédimentent sous l’action de la gravité. Des images dynamiques sont obtenues par un système vidéo-microscopique en plaçant la suspension d’érythrocyte dans un plasma à 5% d’hématocrite à l’intérieur d’une chambre de verre de 100 µm d’épaisseur. Les images sont enregistrées pendant30sec à 2 minutes d’intervalle. Après numérisation, les images fortement bruitées sont traitées pour les corriger de leurs défauts de luminance, d’entrelacement vidéo et de bruit de fond. Un post-traitement est appliqué pour rehausser et détecter les contours. Pour mesurer le processus d’agrégation, des paramètres morphométriques sont étudiés: aire projetée de l’agrégat (PAA), périmètre projeté de l’agrégat (PAP), facteur de forme (FF), temps de sédimentation (SCT). Pour déterminer la variabilité de l’agrégation érythrocytaire avec le vieillissement, des échantillons de sang provenant de sujets d’âge divers (20 à 60 ans) sont analysés. L’analyse montre que les PAA, les PAP et les FF, et les SCT diminuent avec l’age surtout chez les sujets de 51-60 ans pour lesquels les agrégats formés sont compacts et sédimentent plus rapidement. © 2004 Elsevier SAS. All rights reserved. Keywords: Human aging; Erythrocyte aggregation; Sedimentation; Dynamic imaging; Morphometric parameters Mots clés : Vieillissement ; Agrégation érythrocytaire ; Sédimentation ; Image dynamique ; Paramètres morphométriques
* Corresponding author. E-mail address: [email protected] (M. Singh). © 2004 Elsevier SAS. All rights reserved. doi:10.1016/j.rbmret.2003.12.002
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1. Introduction Erythrocyte aggregation is an important phenomenon occurring in the blood circulation at micro-circulatory level. Aggregates are formed due to adsorption of plasma proteins such as fibrinogen and globulin or macromolecules (dextrans) on erythrocyte membrane, leading to the formation of three-dimensional chain-like structures. These are formed under low flow or stasis condition and are completely disaggregated into individual cells under rapid flow conditions [1]. The alterations of erythrocyte aggregation in diseased conditions could be detrimental to blood circulation, causing increased risk of cardiovascular and cerebrovascular disorders [2]. Hence, precise monitoring of this process is of clinical importance for better understanding of the pathophysiology of the diseases and to supplement the diagnostic parameters [3]. For indirect quantification of erythrocyte aggregation, the erythrocyte sedimentation rate (ESR) is occasionally used [1]. But it is a time consuming method and does not provide the precise details of the aggregation process. Microscopic technique, based on direct observation of the aggregates under stasis conditions, is represented in terms of number of aggregates per unit volume [1]. Increase in the apparent viscosity of blood at low shear rate, as determined by viscometry, is partly attributed to the erythrocyte aggregation [4]. These aggregates are also visualized by rheoscopic method while measuring the blood viscosity at various shear rates [4,5]. These measurements are carried out either under stasis conditions or by shearing at high shear rate followed by bringing the sample to near stasis conditions. The laser aggregometry techniques employ continuous recording of backscattered or transmitted light signal from the suspension of erythrocytes [6–8]. Similar to laser scattering signals the erythrocyte aggregation process is further analyzed by processing of the ultrasound backscattered signals [9–10]. By applying the pattern recognition procedures on backscattered ultrasound Doppler signals the size of the aggregate is determined [11]. The quantification of the aggregation process under dynamic conditions is further carried out in terms of various aggregation parameters as determined by the changes in the transmitted light intensity [12]. Despite the versatility of the latter technique the data on morphological changes, required to provide the details of the cellular bonding and shape alteration, could not be obtained. Maeda et al. [13] obtained the rheoscopic images under stasis or low shear rate conditions and quantified the growth rate of aggregates. Chen et al. [14] generated the flow of erythrocyte suspension in a glass chamber under low flow conditions to analyze the variation in size and shape of aggregates. The formation of three-dimensional aggregate structure requires very low shear conditions, similar to micro-circulation. Such a dynamic condition could be achieved during the gravitational sedimentation of the aggregates at an appropriate sample hematocrit, and the formation and movement of these could be monitored and recorded by a video-microscopic system.
In contrast to conventional techniques the images obtained by this procedure are associated with high background noise, which require sophisticated image processing procedures in order to obtain the desired information. Therefore, the present work is aimed to develop a computerized system for dynamic analysis of aggregation of erythrocytes from their sequential images obtained under gravitational sedimentation conditions. During human aging the blood constituents are subjected to various changes, which may affect the formation and other characteristics of the aggregates. By application of this technique the changes in aggregation process are further analyzed. 2. Materials and methods 2.1. Experimental technique Fig. 1 shows the block diagram of the aggregates imaging system. To record data the microscope (Leitz Dilux22, Germany) in transmission mode, with NPL Fluotar objective of magnification 40× (phase contrast), was used. A wellcollimated beam of light from a 12 V 100 W tungsten halogen lamp, by using a field diaphragm and by incorporating an interchangeable condenser, was achieved. This combination was ideal for recording of aggregates in the sample chamber, which was vertically mounted on the microscope stage, achieved by placing the microscope horizontally on a specially designed supporting stand. The dimensions of sample chamber, made of optically flat glass slide, were 45 × 13 × 0.1 mm. The gap between vertical plates of 100 µm allowed the free movement of erythrocytes and their formed aggregates. For recording of images in a video cassette by a VHS video cassette recorder (National NV-370, Japan) at a rate 25 fps the microscope was fitted with color video camera system (VK 4003, Philips Newvicon, The Netherlands). These images were continuously monitored by displaying on a monitor (Philips Match-line Video RGB Monitor, The Netherlands). 2.2. Sample preparation Healthy subjects, without any clinical disease and normal plasma and serum biochemical levels, of age ranging from 20 to 60 years, were selected. These were divided into four groups: 20–30, 31–40, 41–50 and 51–60 years in Groups I–IV, respectively. Fresh blood sample from each subject was collected by venepuncture in a test tube containing citrate phosphate dextrose (10:1.4), as an anticoagulant. Each sample was centrifuged at 3000 g for 20 min. Thereafter, the supernatant plasma was separated and the buffy layer on top of the cells was gently removed and discarded. A suspension of 5% hematocrit was prepared in the plasma for imaging of aggregation process of erythrocytes. 2.3. Data acquisition A well-mixed erythrocyte suspension was placed in the glass chamber and was vertically mounted on microscope
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Fig. 1. Schematic of (a) Aggregation imaging system, and (b) sample chamber.
stage. The field of view was adjusted at the center both with respect to the height and sidewalls of the chamber. The sequential images of aggregates and individual erythrocytes during sedimentation were recorded on a video cassette recorder for a duration of 30-s after every 2.0 min interval till the end of process, at room temperature 25 ± 1 °C. These data after digitization with Matrox frame capture card (Matrox Marvel G400-TV) were stored in the computer for further processing. 2.4. Data analysis The flow chart of image processing and analysis procedures is shown in Fig. 2. Image processing was carried out using Matlab software package for a given sequence of images that were recorded under identical conditions. The initialization of the program involved setting of image counter, number of images to be processed, type of image, the path, source and destination folder names from where the images were to be read and stored. Each image from the sequence was loaded and processed to get a region of interest (ROI) mask and its corresponding image parameters, which were stored in the destination folder at the end of the analysis. With the processing of each image, the image counter was incremented and then compared with the number of images to check if all the images were processed. The same routine was repeated with the next image. The preprocessing steps involved illumination correction, video de-interlacing, background subtraction, and deblur-
ring. Usually the images after digitization had poor or nonuniform illumination, which was corrected by enhancing the image using histogram equalization [15]. The digitized images were further associated with odd fields, which look like vanishing blind strips, and noise. These effects from the images were removed by video de-interlacing, by using a rotational symmetry Gaussian filter [15]. This operation was followed by subtraction of the background from this image, which was recorded separately under exactly identical conditions. The minimization of image degradation due to motion blur, using Lucy–Richard blind deblurring algorithm, was carried out [16]. The restored image was found to be satisfactory after 10 iterations. The post-processing of the image involved edge enhancement, thresholding, median filtering and edge detection. The resulting image after preprocessing was first edge enhanced and thresholded by Otsu’s method [17], by using a global gray level threshold followed by median filtering. The edge detection of the filtered image was carried out by Canny’s edge detection scheme [18]. A ROI analysis using morphological reconstruction method [19] to process the edgedetected image was further used. For this purpose, an ROI mask was developed which exactly matched the aggregate and cellular areas in the original image. The ROI of this image was filled with white pixels. The filled image that matches the ROI of the original image was further labeled [20] and was used for determining the aggregate parameters. The resulting data were stored in a data file.
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of all the objects. The ‘Pi’, the perimeter of the ith object, in terms of number of pixel was equal to the sum of all the pixels that belonged to this object, i.e., Pi = 兺 f(x, y), x, y { i (4) Size distribution of aggregates (SDA): To obtain the size and spatial distribution of all aggregates, based on above data, the projected aggregate size with respect to aggregates number was plotted. (5) Sedimentation completion time (SCT): Time duration between the first frame and the last frame with the PAA of the sedimenting aggregates approaching to the desired minimum size of area (10,000 ± 1000) pixels. (6) Form factor (FF): Calculated as (PPA)2/4p(PAA). 3. Results
Fig. 2. Sequence of image processing procedures.
2.5. Analysis of aggregate parameters From the processed images following parameters were calculated: (1) Number of aggregates: The number of labeled objects in the image corresponded to the number of aggregates and single cells. The counting of objects was carried out by a row-wise scan based on neighborhood connectivity [15]. (2) Projected area of aggregate (PAA): The area of each aggregate/individual cell was the number of pixels in that labeled object. The PAA of a given image was given by the sum of the areas of all the objects. Thus ‘Ai’ the area was equal to the sum of all the pixels that belong to ith object [21] Ai = 兺 f(x, y),x, y { i (3) Projected perimeter of aggregate (PPA): For this purpose, the contour of the ROI-filled image was first extracted and was labeled in the same manner as above. The perimeter of each aggregate/individual cell was the number of the pixels on the contour of that labeled object. The PPA was the sum of the perimeters
Fig. 3 shows the images after implementation of various steps of image processing. The original image (a) is after the correction of illumination and de-interlacing. The background subtraction is carried out in gray mode. The constant noisy background that can be seen in original image is effectively removed from this image (b). By deblurring the image, edges of the cells and aggregates are well observed (c). The thresholded image shows that the aggregates are clearly delineated from the rest of the image (d). The edge of the image shows many discontinuities, which depend on the image quality and the threshold chosen for edge detection (e). In order to get continuous edge, morphological contour structuring is employed wherein the image edge is subjected to a deliberate over-dilation by a known size of line-structuring element. In the dilated image (f) the discontinuities in the edge are effectively removed, but the ROI is blown up because of dilation. In order to recover the original ROI, the dilated image is eroded by the same size of the linestructuring element, employed for dilation. The eroded image shows that the edge is continuous and the original ROI is restored (g). The ROI image is further filled with white pixels for the PAA determination (h). Thus by the present contour restructuring the automation of the processing of whole sequence of images, without the necessity of interactive thresholding and edge detection, could be possible. To establish that the ROI is successfully extracted from the original image, the final image (h) is inverted and is overlapped on the deblurred image (c) to get the masked image (i), which shows close match of the area of interest as in the original image. Fig. 4 shows the examples of the ROI images of aggregates and their size distribution in subjects of different age groups after an interval of 16 min. From these, it is evident that the pattern of aggregate images and their size distribution depend on the age of the subject. Large number of aggregates of varying sizes are observed in subject of age 20 year, whereas, this is the minimum in subject of age 53 year. Even at constant hematocrit, the formed aggregates of elderly subject sediment faster, indicating that these are large size but compact aggregates.
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Fig. 3. Sequence of processed images. (a) Original image after illumination, correction and de-interlacing, (b) background subtracted image with edge enhancement, (c) deblurred image, (d) thresholded image, (e) edge image, (f) dilated image, (g) eroded image, (h) ROI image, (i) mask of ROI and deblurred image.
From the processed images of the aggregates, the PAAs of various samples are calculated. Fig. 5 shows the variation in the mean PAA of samples of different age groups, as determined sequentially throughout the sedimentation process. Initially the sedimenting aggregates are of larger size but with progression of time their size is reduced. The SCT is the maximum for age Group I and the minimum for subjects of Group IV. The change in the PPA represents the change in its contour. Fig. 6 shows the variation in the PPA of aggregates in various age groups. The FF of aggregates, which determines the compactness, indicates that a loosely bound aggregate correspond to high PPA, whereas, a compact one correspond to its low value. The variation of the SCT, PAA, PPA and FF are given in Table 1. These parameters complement each other and vary significantly in Group IV compared to Group I. In Group III, the change is marginally significant and not significant for Group II. The shorter SCT is associated with lesser PPA and PAA. The FF is the minimum for Group IV and is the maximum in Group I.
Table 1 Comparison of sedimentation completion time (SCT), projected area of aggregate (PAA), and projected perimeter of aggregate (PPA), and form factor (FF) different age group subjects with control group (20–30 years). Number of subjects in each group = 5 Age group (years) 20–30 31–40 41–50 51–60 a
SCT a (min)
PAA (pixels)
PPA (pixels)
FF
5230 ± 529 4940 ± 481 3800 ± 345 * 3480 ± 256 **
57.26 ± 5.1 52.85 ± 5.4 43.21 ± 4.1 * 40.74 ± 5.1 **
After 16 min #
38 ± 3 36 ± 3 28 ± 4 * 26 ± 4 **
38030 ± 4343 36757 ± 3412 26601 ± 2312 * 23663 ± 1234 **
Mean ± S.D., * P = 0.1, ** P = 0.05, # = control
4. Discussion The present study involves the development of this technique for recording and processing of the sequence of images throughout the sedimentation process, and to compute the shape-related parameters. The image analysis is automated for a set of images, which are recorded and processed under
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Fig. 4. The ROI images and aggregate size distribution of subjects of different age groups at t = 16 min.
identical conditions. The contour restructuring technique employed in the image processing makes the automation of image analysis possible for the entire sequence of images without the necessity of interactive thresholding and edge detection. As and when required, the process can be interrupted. The measurement technique, unlike other methods, involves two aspects—visualization and quantification in terms of image and shape-related parameters under dynamic conditions. The variation of the projected area and perimeter of the aggregates and their FF quantifies this process. This technique through imaging and calculation of its related parameters elaborates the roles of various plasma and cellular parameters. One of the major factors in enhancing the erythrocyte aggregation is plasma fibrinogen, which is also responsible in enhancing erythrocyte aggregation in elderly subjects [1,22–28], which quantify the change in terms of the PAA and PPA. The change in the FF is generally
applied to calculate the deviation from the discoidal shape of an individual erythrocyte. This aspect has recently been applied to relate the cholesterol-enrichment in erythrocyte membranes [29]. But in aggregation process the aggregates do not show a well-defined shape. Hence the FF could not be used to show the marked deviation from a normal discoidal erythrocyte shape. The human aging is a biological process. There are several changes in the system associated with this process. To show the variation due to this process the blood samples are obtained from subjects of various age groups. The aggregation of erythrocytes, as determined by conventional methods, also shows an increasing pattern in subjects of age group 50– 65 compared to subjects of younger group (between 20 and 30 year) [29,30]. Our present findings based on shape descriptors are not only in agreement with these but also with our results obtained by laser light aggregometer [31]. The
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technique can be implemented in routine physical and clinical analysis, erythrocyte–drug interaction and monitoring of patients’ progress after drug therapy.
References [1]
[2] [3] [4]
[5]
[6] [7] [8]
[9]
[10]
[11] Fig. 5. Mean projected area of aggregates of subjects of different age groups. [12]
[13]
[14]
[15] [16] Fig. 6. Mean projected perimeter of aggregates of subjects of different age groups.
[17]
enhanced aggregation in aging population is primarily due to the increased level of the plasma proteins [30]. Such changes in the elderly may lead to greater risk of cardiovascular and cerebrovascular diseases [32].
[19]
In conclusion, this technique is simple and of high reproducibility, which provides the visual and quantitative details of the formed erythrocyte aggregates. With these features this
[18]
[20] [21] [22]
Chien S. Biophysical behavior of red cells in suspension. In: Surgenor DM, editor. The Red Blood Cell, 2. New York: Academic Press; 1975. p. 1032–133. Muller R. Hemorheology and peripheral vascular diseases: a new therapeutic approach. J Med 1981;12:209–36. Puniyani R. Clinical Hemorheology: New Horizons. New Delhi: New Age International; 1996. Schmid-Schoenbein H, Wells R, Schildkraut R. Microscopic and viscometry of blood flowing under uniform shear rate (rheoscope). J Appl Physiol 1969;26:674–8. Schmid-Schoenbein H, Malotta H, Streisow F. Erythrocyte aggregation, causes, consequences and methods of assessment. Tijdschr NVKC 1990;15:619–25. Gaspar RA, Thurston GB. Erythrocyte aggregate rheology by transmitted and reflected light. Biorheology 1988;25:471–87. Stoltz JF, Paulus F, Donner M. Experimental approaches to erythrocyte aggregation. Clin Hemorheol 1987;7:109–18. Hardeman MR, Goedhart PT, Dobbe JGG, Lettings KP. Laser-assisted optical rotational cell analyzer: a new instrument for measurement of various structural hemorheological parameters. Clin Hemorheol 1994;14:605–18. Swarnamani S, Singh M. Analysis of erythrocyte aggregation mechanism in presence of dextrans and magnetic field by ultrasound scattering in blood. Biorheology 1989;26:847–62. Boynard M, Lelievre JC. Size determination of red cells aggregates induced by dextran using ultrasound backscattering phenomenon. Biorheology 1990;27:39–46. Karabetsos E, Papaodysseus C, Kautsouris D. A new method for measuring red blood cell aggregation using pattern recognition techniques on backscattered ultrasound Doppler signals. Clin Hemorheol Microcir 1999;20:63–75. Singh M, Kumaravel M. A computerized system for sequential analysis of aggregation of erythrocyte under dynamic conditions. Comput Biomed Res 1994;27:325–36. Shiga T, Maeda N. Kinetic study on aggregation of red blood cells by successive analysis of moving video images. In: Stoltz JF, Donner M, Copley AL, editors. Hemorheologie et Agregation Erythrocytaire, 3. Paris: E. M. Inter.; 1991. p. 38–45. Chen S, Barshtein G, Gavish B, MahlerY,Yedgar S. Monitoring of red blood cell aggregability in a flow chamber by computerized image analysis. Clin Hemorheol 1994;14:497–508. Gonzalez RC, Wood RE. Digital Image Processing. New York: Addison-Wesley; 1999. Lucy LB. An iterative technique for the rectification of observed images. Astronom J 1974;79:8–37. Otsu NA. Threshold selection method from gray-level histograms, IEEE Trans. System Man Cybernet 1979;9:62–6. Canny J. A computational approach to edge detection, IEEE Trans Pattern Analy. Machine Intell 1986;8:679–98. Soille P. Morphological Image Analysis: Principles and Applications. Berlin: Springer-Verlag; 1999. Haralick RM, Shapiro LG. Computer and Robot Vision, I. New York: Addison-Wesley; 1992. Rosenfeld A. Iterative methods in image analysis. Pattern Recogn 1978;10:1811–70. Sanjay J, Singh M. Computerized descriptors for analysis of hypercholestromic cells. Proc Intern Conf Biomed Eng Chennai 2001:291–3.
74
S. Jayavanth, M. Singh / ITBM-RBM 25 (2004) 67–74
[23] Skalak R. Aggregation and disaggregation of red blood cells. Biorheology 1984;23:463–76. [24] Tarallo P, Henry J, Gueguen R, Siest G. Reference limits for plasma fibrinogen. Europ J Clin Chem Clin Biochem 1992;30:745–51. [25] Barasch E, Benderly M, Graff E, Behar S, Reicher-Reiss H, Caspi A, Pelled B, Reisin L, Rouguin N, Goldbourt U. Plasma fibrinogen level and their correlate in 6457 coronary heart disease patients, The Bezafibrate Infraction Prevention study. J Clin Epidemiol 1995;48: 757–65. [26] Stoltz JF, Singh M, Riha P. Hemorheology in Practice. Amsterdam: IOB Press; 1999. [27] Avellone G, Garbo D, Panno AV, Cordova R, Alletto G, Raneli G, De Simone R, Strano A, Bompani GD. Hemorheological components in pre-geriatric and geriatric age range in a randomly selected western Sicily population sample (Casteldaccia Study). Clin Hemorheol 1993; 13:83–92.
[28] Mchedlishvili G. Disturbed blood flow structuring as critical factor of hemorheological disorders in microcirculation. Clin Hemorheol Microcir 1998;19:315–25. [29] Manjunatha M, Singh M. Digital analysis of induced erythrocyte changes in hypercholesterolemia under in vitro conditions. Curr Sci 2000;79:1588–91. [30] Ajmani RS, Rifkind JM. Hemorheological changes during human aging. Gerontology 1998;44:111–20. [31] Sanjay J, Singh M. Changes in erythrocyte aggregation with human aging. Curr Sci 2002;82:191–6. [32] Banerjee AK, Pearson J, Gilliland EL, Goss D, Luwis JD, Striling Y, Maeda TW. A six year prospective study of fibrinogen and other risk factors associated with the mortality in stable claudicents. Thrombo Haemost 1992;68:261–3.