- Open Access
Automated migration analysis based on cell texture: method & reliability
© Qin et al; licensee BioMed Central Ltd. 2005
- Received: 07 September 2004
- Accepted: 03 March 2005
- Published: 03 March 2005
In this paper, we present and validate a way to measure automatically the extent of cell migration based on automated examination of a series of digital photographs. It was designed specifically to identify the impact of Second Hand Smoke (SHS) on endothelial cell migration but has broader applications. The analysis has two stages: (1) preprocessing of image texture, and (2) migration analysis.
The output is a graphic overlay that indicates the front lines of cell migration superimposed on each original image, with automated reporting of the distance traversed vs. time. Expert preference compares to manual placement of leading edge shows complete equivalence of automated vs. manual leading edge definition for cell migration measurement.
Our method is indistinguishable from careful manual determinations of cell front lines, with the advantages of full automation, objectivity, and speed.
- Cell Migration
- Domain Expert
- Front Line
- Endothelial Cell Migration
- Second Hand Smoke
In particular, we are interested in the effects of SHS on endothelial migration. By comparing automated migration analysis with varied exposure to SHS, for cells with and without specific genes, we can examine why exposure to SHS impairs endothelial cell migration and explore possible cures [3, 4].
The automated borders are compared blindly by a team of domain experts to manual borders created by a technician to assess accuracy. Results are also evaluated blind to biologic significance to determine concordance and power to demonstrated biological effects.
Biologists deal with this by making multiple manual measurements, to report an average. Observers have difficulty deciding where and how many times to measure the width. Besides, there are many pairs of images to be processed. Therefore, automatic measurement is desired.
The primary difference between cell-populated areas and the clear lane is texture. The cell-populated areas are speckled with cells, the clear lane is not.
In order to capture the cellularity characteristic of the source images, we sought to compute a texture index that would emphasize the cellular attribute of the region of interest and also minimize the influence of non-cellular signal variations [11–14]. Because the image may have non-uniform background where the "clear lane" can be 'darker' than the 'cells' at other locations, the texture index should be generated from the relative gray value difference. Furthermore, we know the orientation of the experimentally produced clear lane, which we take to be vertical. Then our algorithm generates the texture in this way:
For each point in the original image
Search for darker point in this line vertically
Set the distance between start and darker point as the gray value of the corresponding point in the texture map
Search for continual darker points and set the distance as the value of them Scale the value to 0–255:
where pv is the new pixel intensity value, cv is the distance value of the corresponding point and max and min are the minimum and maximum distance value.
Based on the texture map, the region we are interested in appears as a white vertical band. Thus the second stage of analysis must determine the position and width of this lane. As the information in each vertical column is equivalent to repeated measures, we can combine the data to marginal projection. From the histogram of this we can compute a classifier for lane vs. cells and determine the half-height width. The locations are then mapped to a graphic overlay on the original image to demarcate the front lines of cell migration. The change in distance between the front lines reports the amount of (bi-front) cell migration.
2. Compute the discriminant classifier (DC) which is average value of P [i].
3. Locate the leading and trailing edges based on classifier crossing. If P [i] < DC and P [i+1] > P [i], i is the leading edge. Conversely, if P [i] > DC and P [i+1] < DC, i is the trailing edge. Then a few pairs of leading and trailing edges could be obtained. The target pair is identified based on the width, P [i] values between leading and trailing edges and "clear lane" location in the time neighboring image.
4. Record locations and generate graphic overlay for original image.
Since the manual assessment is the research gold standard for image processing [15–18], a technologist specially trained to identify the leading and trailing edges of cell migration was provided a computer tool to mark those edges manually in a manner compatible with the graphics overlay engine. These are called "manual edges." The manual edges and the automated edges were then presented to a team of domain experts in random order pairs (one of each on corresponding image) for preference scoring. The scores ranged from 1–5, where 1 is strong preference for first overlay, 2 mild preference for first overlay, 3 equivalency, 4 mild preference for second overlay, and 5 strong preference for second overlay. Results were analyzed by Kappa statistic as a measure of agreement.
The results of domain expert preference by quality for automated vs. manual assignation of migration front lines, evaluated blinded to method, randomized, and subsequently decoded. Overall, there is complete equivalence of automated vs. manual with respect to expert preference for quality. The values ranged 2–4. In no cases was manual strongly preferred over automatic. Preference testing of analysis methods showed near equivalence, favoring preference for the automated borders (3.02 ± 0.11). Agreement between observers in preference was examined for two domain experts, revealing good agreement (Kappa = 0.59, p < 0.003). Agreement in preferences by a technologist without domain expertise was lower (Kappa = 0.23, 0.25, p > 0.10) but supported the same conclusion: the automated analysis is at least as good as manual selection by domain experts.
Our migration analysis is based on the texture index of the images. This index should reflect the attribute of the images. Since no global thresholding technique could be used in our images, the segmentation of regions and boundaries (edges) have to consider the local property . Because the target boundaries always show as a vertical band, the line-based segmentation appears to be the most suitable approach for our task. Further analysis of regions and edges is based on a uniform data structure reflecting the texture character in each column.
Our results show a robust automatic method with no detected errors. This study is a pilot study demonstrating feasibility and biologic significance in real application. Further collective experience in multi-center applications are needed to establish the full utility of the method.
In addition, the program runs on the software platform, ImageJ  and the speed is fast. A normal process time for one study of images is less than 3 minutes. Results such as width and percentage can be shown as a table. It offers a convenient way for researcher to process their image data using excel.
We describe a novel method of cell migration analysis based on texture pre-processing and discriminant analysis. Domain expert preference testing demonstrates that this automated method compares favorably to the much more painstaking manual method.
The further study is to apply this to evaluation of the impact of SHS on endothelial cell migration. For that purpose, we have constructed a SHS capture system in which we bubble the SHS through tissue culture medium to assess its impact on cell migration. Our results indicate that this analysis system is very sensitive to biological effects, documenting that SHS impairs cell migration [19–22].
Project name: Cell migration measurement project
Project home page: http://magic.hitchcock.org/jianfeng/index.html
Operating system(s): Platform independent
Programming language: Java
Other requirements: Java 1.3.1 or higher, ImageJ
Any restrictions to use by non-academics: Licence needed
Funded in part by FAMRI.
- Michiels C: Endothelial cell functions. J Cell Physiol. 2003, 196 (3): 430-43. 10.1002/jcp.10333.View ArticlePubMedGoogle Scholar
- Sumpio BE, Riley JT, Dardik A: Cells in focus: endothelial cell. Int J Biochem Cell Biol. 2002, 34 (12): 1508-12. 10.1016/S1357-2725(02)00075-4.View ArticlePubMedGoogle Scholar
- Hutchison SJ, Sievers RE, Zhu BQ, Sun YP, Stewart DJ, Parmley WW, Chatterjee K: Secondhand tobacco smoke impairs rabbit pulmonary artery endothelium-dependent relaxation. Chest. 2001, 120 (6): 2004-12. 10.1378/chest.120.6.2004.View ArticlePubMedGoogle Scholar
- Hutchison SJ, Sudhir K, Sievers RE, Zhu BQ, Sun YP, Chou TM, Chatterjee K, Deedwania PC, Cooke JP, Glantz SA, Parmley WW: Effects of L-arginine on atherogenesis and endothelial dysfunction due to secondhand smoke. Hypertension. 1999, 34 (1): 44-50.View ArticlePubMedGoogle Scholar
- Cai G, Lian J, Shapiro SS, Beacham DA: Evaluation of endothelial cell migration with a novel in vitro assay system. Methods Cell Sci. 2000, 22 (2–3): 107-14. 10.1023/A:1009864613566.View ArticlePubMedGoogle Scholar
- Wang A, Nomura M, Patan S, Ware JA: Inhibition of protein kinase Calpha prevents endothelial cell migration and vascular tube formation in vitro and myocardial neovascularization in vivo. Circ Res. 2002, 90 (5): 609-16. 10.1161/01.RES.0000012503.30315.E8.View ArticlePubMedGoogle Scholar
- Lingen MW: Endothelial cell migration assay. A quantitative assay for prediction of in vivo biology. Methods Mol Med. 2003, 78: 337-47.PubMedGoogle Scholar
- Maliakal JC: Quantitative high throughput endothelial cell migration and invasion assay system. Methods Enzymol. 2002, 352: 175-82.View ArticlePubMedGoogle Scholar
- Beil M, Irinopoulou T, Vassy J, Wolf G: A dual approach to structural texture analysis in microscopic cell images. Comput Methods Programs Biomed. 1995, 48 (3): 211-9. 10.1016/0169-2607(96)81866-9.View ArticlePubMedGoogle Scholar
- ImageJ SW: [http://rsb.info.nih.gov/ij/]
- Malpica N, Santos A, Tejedor A, Torres A, Castilla M, Garcia-Barreno P, Desco M: Automatic quantification of viability in epithelial cell cultures by texture analysis. J Microsc. 2003, 209 (Pt 1): 34-40. 10.1046/j.1365-2818.2003.01094.x.View ArticlePubMedGoogle Scholar
- Mojsilovic A, Popovic M, Amodaj N, Babic R, Ostojic M: Automatic segmentation of intravascular ultrasound images: a texture-based approach. Ann Biomed Eng. 1997, 25 (6): 1059-71.View ArticlePubMedGoogle Scholar
- Haralick R, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans Systems, Man Cybernetics. 1973, 3: 610-621.View ArticleGoogle Scholar
- Baba N, Ichise N, Tanaka T: Image area extraction of biological objects from a thin section image by statistical texture analysis. J Electron Microsc (Tokyo). 1996, 45 (4): 298-306.View ArticleGoogle Scholar
- Henry JA, Flick CL, Gilbert A, Ellingson RM, Fausti SA: Comparison of manual and computer-automated procedures for tinnitus pitch-matching. J Rehabil Res Dev. 2004, 41 (2): 121-38.View ArticlePubMedGoogle Scholar
- Danilouchkine MG, Westenberg JJ, Reiber JH, Lelieveldt BP: Automated short-axis cardiac magnetic resonance image acquisitions: accuracy of left ventricular dimension measurements in normal subjects and patients. Invest Radiol. 2004, 39 (12): 747-55. 10.1097/00004424-200412000-00006.View ArticlePubMedGoogle Scholar
- Positano V, Gastaldelli A, Sironi AM, Santarelli MF, Lombardi M, Landini L: An accurate and robust method for unsupervised assessment of abdominal fat by MRI. J Magn Reson Imaging. 2004, 20 (4): 684-9. 10.1002/jmri.20167.View ArticlePubMedGoogle Scholar
- Tan P, Hamilton CA, Link KM, Kitzman DW, Hundley WG.: Automated analysis of phase-contrast magnetic resonance images in the assessment of endothelium-dependent flow-mediated dilation. J Cardiovasc Magn Reson. 2003, 5 (2): 325-32. 10.1081/JCMR-120019416.View ArticlePubMedGoogle Scholar
- Shin VY, Liu ES, Koo MW, Luo JC, So WH, Cho CH: Nicotine suppresses gastric wound repair via the inhibition of polyamine and K(+) channel expression. European Journal of Pharmacology. 2002, 444 (1–2): 115-21. 10.1016/S0014-2999(02)01610-2.View ArticlePubMedGoogle Scholar
- Shin VY, Liu ES, Koo MW, Wang JY, Matsui H, Cho CH: Cigarette smoke extracts delay wound healing in the stomach: involvement of polyamine synthesis. Experimental Biology & Medicine. 2002, 227 (2): 114-24.Google Scholar
- Wong LS, Green HM, Feugate JE, Yadav MN, Nothnagel EA, Martins-Green M: Effects of "second-hand" smoke on structure and function of fibroblasts, cells that are critical for tissue repair and remodeling. BMC Cell Biology. 2004, 5 (1): 5-10.1186/1471-2121-5-13.View ArticleGoogle Scholar
- Snajdar RM, Busuttil SJ, Averbook A, Graham DJ: Inhibition of endothelial cell migration by cigarette smoke condensate. Journal of Surgical Research. 2001, 96 (1): 10-6. 10.1006/jsre.2000.6055.View ArticlePubMedGoogle Scholar
- Gao Y, Li M, Chen W, Simons M: Synectin, Syndecan-4 Cytoplasmic Domain Binding PDZ Protein, Inhibits cell migration. J Cell Physiol. 2000, 184: 373-379. 10.1002/1097-4652(200009)184:3<373::AID-JCP12>3.0.CO;2-I.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.