- Email: [email protected]
Extended depth of focus image for phytolith analysis
Journal of Archaeological Science 36 (2009) 2253–2257
Contents lists available at ScienceDirect
Journal of Archaeological Science journal homepage: http://www.elsevier.com/locate/jas
Extended depth of focus image for phytolith analysis Yan Wu*, Changsui Wang Department of Scientific History and Archaeometry, Graduate School of Chinese Academy of Sciences, Beijing, 100049, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 January 2009 Received in revised form 7 June 2009 Accepted 14 June 2009
This paper proposes a new phytolith analysis method by extended depth of focus technology. The extended depth of focus image is sharp throughout by the image processing of a series of photos, while each acquisition of the original photo will be compromised and show certain parts of the specimen in and out of focus. Consequently, the extended depth of focus image is suitable for computer-assisted morphometrics analysis. Meanwhile, the extended depth of focus image can be used for three-dimensional reconstruction images. Experiments on rice husk multi-cell panel show that the three-dimensional reconstruction image method can reconstruct multiple double-peaked phytoliths at one time. We also measure the parameters of multiple double-peaked phytoliths from the reconstructed threedimension image. Comparative experiments show that the reconstructed three-dimensional image is precise enough for rice identification. Based on the above experiments, we recommend the extended depth of focus method as a promising tool in phytolith research. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Archaeology Phytolith Extended depth of focus Image analysis
1. Introduction Phytoliths are opaline silica within and between cells formed in living plants by monosilicic acid brought into the plant through the uptake of water. Since phytoliths can create replicas of the plant cell bodies, phytoliths can be used to identify a certain genus or species according to its shape, size, ornaments, cell orientation and other anatomical features (e.g., Twiss, 1987; Piperno, 1988). Since the 1970s, phytolith analysis has been a fast developing discipline in the field of archaeology. They serve as efficient clues to paleoenvironmental reconstruction, agricultural activities and human activities through analysis and interpretation of phytolith assemblages from various sediments (e.g., Ball, 1999; Bowery, 2001; Horrocks, 2000; Pearsall, 2000, 2002; Piperno, 1988, 2006; Rosen, 1992). Most initial phytolith analysis attempts are based on the typological approach, where the frequency of types and/or shapes of phytoliths were used as classification criteria. However, a purely typologic approach is rarely enough to distinguish between grass taxa at the species level (Ball and Brotherson, 1992; Rovner and Russ, 1992). Morphometrics (measurements of size and shape) were proved to be useful in conjunction with typologies to improve the phytolith taxonomic resolution. The morphometric parameters can be directly measured by an eyepiece micrometer in the microscope (Pearsall, 1978; Piperno, 1984). Many researchers then used computer-assisted image analysis to acquire the morphometric parameters (Russ and Rovner, 1987; Ball, 1996). Because the * Corresponding author. Tel.: þ86 15810231703. E-mail address: [email protected] (Y. Wu). 0305-4403/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jas.2009.06.010
same phytolith may have two different shapes when viewed from different angles, making identification subjective and somewhat tricky (Piperno, 1988), there can be potential problems using 2D micrographs for typological analysis and morphometrics measurement. Thus, the authors propose to reconstruct a threedimensional image from a pair of two dimension photos taken under scan electron microscope (e.g., Wu, 2006). Most reported work using computer-assisted image analysis is based on scanning electron micrographs. However, the scanning electron microscope is expensive and difficult to operate, which limits its application in daily research. In comparison, the optical microscope is much cheaper and easier to operate. Image analysis based on the optical microscope will have broader applications. However, one old and familiar problem of optical microscopes is the limited depth of focus. In this paper, we propose to use extended depth of focus (EDF) technology for phytolith analysis. The EDF image can be used for statistical analysis. Meanwhile, a three-dimensional reconstruction image can be reconstructed from the EDF image, which can be used for morphological analysis and parameter measuring. Experiments show that the three-dimension image reconstruction method is promising in reducing the subjectivity of phytolith identification and improving the efficiency of measurement.
2. Extended depth of focus principle and application A lack of depth of focus in microscope images is a common problem in biological imaging with conventional optical microscopy.
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Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
Fig. 3. Original image taken under optical microscope.
Fig. 1. 3D reconstruction principle from optical microscope.
Fig. 2. Original image taken under optical microscope.
When the specimen surface is irregular, portions of the object’s surface outside the focal plane will appear defocused. Consequently, each acquired image can be composed of certain parts of the specimen that are in and out of focus. To reconstruct an image that is sharp everywhere, multiple images are taken at different focal planes. The challenge then becomes to select from each slice the area that is focused in order to reconstruct an image that is sharp everywhere (Manjunath, 1995; Valdecasas, 2001, 2002; Forster, 2004). In this way, it is possible to extend the depth of focus without the physical limitation of the numerical aperture of the objective lens. One of the known methods is based on the wavelet transform (Forster, 2004). Multiple microscope systems provide EDF image generation function and corresponding 3D reconstruction function by EDF technology, such as the Soft Imaging System by the Gmb H Company and the VE-8800 Microscope system by the Keyence Company. In the Soft Imaging System by the Gmb H Company, the systems first capture a focus image series with an even interval of focus. Then, the system goes through the whole image series pixel by pixel (to be more precise, EDF goes through all the groupings of pixels),
Fig. 4. Extended depth of focus image.
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
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Fig. 5. Extended depth of focus image of common millet phytolith.
and determines which image contains the pixel(s) that is most focused (for each particular image position) out of all the images of the series. Each image is assigned a specific numerical value, which represents a gray value and is entered as such into a gray-value image at the respective image location. Upon completion of this algorithm a complete gray-value image will be ready to describe which image positions/pixel groupings from which images of the series are to be included in the (infinitely) sharp EDF image. Meanwhile, the selected areas of each individual image indicate in which height range the area of the specimen surface is. This is why the gray-value image is
also a qualitative height map of the specimen surface. Thus we can reconstruct the 3D image from this gray-value image, as shown in Fig. 1. In our practice, to acquire images series for 3D reconstruction, we usually put the most interested area in the middle and observe from the top-down angle.
Fig. 6. EDF image.
Fig. 7. Reconstructed 3D image.
3. Extended depth of focus image for morphometrics analysis When the phytolith surface is irregular, portions of the object’s surface outside the focal plane will appear defocused. It is not easy to make a statistical analysis of a phytolith with the original image
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Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257 Table 1 Comparison of measured values.
Fig. 8. Reconstructed 3D image.
taken under the optical microscope, as shown in Figs. 2 and 3. Here, we generate an extended depth of focus image generated from 21 original images with even intervals with the Soft Imaging System (Gmb H Company), as shown in Fig. 4. Obviously, it is easier and more accurate to make a statistical analysis with the extended depth of focus image. Furthermore, the extended depth of focus image can also be archived for future examination and for education. Meanwhile, the EDF image can also be used to analyze the details of a single phytolith. Shown in Fig. 5a,b,c are three images taken under the optical microscope, while Fig. 5d is an extended depth of focus image. Fig. 5d is sharp everywhere while Fig. 5a,b,c all have some portions defocused. 4. Three-dimension reconstruction by extended depth of focus image We chose a rice husk multi-cell panel taken from the Shangzhou site as a sample for 3D reconstruction by EDF image. The doublepeak parameters of bi-peak-tubercle structure are important clues for rice identification. In many sites, we can easily find rice husk multi-cell panels. If we can measure the double-peak parameters in rice husk multi-cell panels, we can greatly improve the efficiency of rice identification. However, it is usually difficult to measure the double-peak parameters in rice husk multi-cell panels directly under the optical microscope because of sheltering by other double-peaks. Researchers often need to tap the slide and hope by good luck to get the right angle. Sometimes, because of unsatisfactory observation angles, researchers may make mistakes in shape identification and parameter measurements. In comparison,
Fig. 9. Reconstructed 3D image.
Measured values under microscope (mm)
Measured values in reconstructed 3D image (mm)
Difference (mm)
36.3 41.1 39.1 28.8 27.6 38.5 48.2 44.8 32.7 29.6 21.8 38.2 36.3 27.9 39.9 37.8 35.7 30.2 33.4 31.4
36.7 41.6 39.8 29.1 27.1 39.1 48.0 44.5 33.3 31.1 22.1 38.4 36.4 28.1 39.6 38.1 35.9 29.8 33.8 31.6
0.4 0.5 0.7 0.3 0.5 0.6 0.2 0.3 0.6 0.5 0.3 0.2 0.1 0.2 0.3 0.3 0.2 0.4 0.4 0.2
it is much more efficient to rotate and shift the reconstructed 3D image to make identifications and measurements. We generated the EDF image and 3D image from 23 original images with the Keyence VE-8800 microscope system. It is well known that the rice husk multi-cell panel originates from the bipeak-tubercle structure of rice. Fig. 6 is the generated EDF image. Figs. 7, 8 and 9 are the reconstructed three-dimensional images of the sample. The graph shows that the three-dimension image reconstruction method can reconstruct multiple peaks at one time. We can also focus on measuring one double-peak by enlarging, rotating and shifting to make more precise judgments and measurements.
5. Initial accuracy analysis In order to verify the precision of parameters measured from a reconstructed 3D image, it is necessary to compare them with the parameters measured directly under an optical microscope. To distinguish ‘domesticated’ rice from ‘wild’ rice is very important for studying the origins and migration of rice. The specific shapes of the phytoliths in rice that distinguish ‘domesticated’ from ‘wild’ are the double-peaked glume cell phytoliths (Zhao et al., 1998). In Zhao’s paper, five measurements were taken for each individual glume cell using an eyepiece micrometer, including width of the top (TW), width of the middle (MW), the height of each peak (H1 and H2) and the depth of the curve (CD). A derived formula based on these five measurements permits correct classification of domesticated rice glume cells in 81.6% of the test cases (Pearsall et al., 1995). Because the top width of double-peaked papilla can be relatively easily measured under the optical microscope, we used this parameter for comparative analysis. Table 1 lists the top widths directly measured under the optical microscope in column 1 and those measured in reconstructed 3D image in column 2. In this comparison, measurement in reconstructed 3D image is accomplished with the VE-8800 microscope system by the Keyence Company. Their differences are listed in column 3. From Table 1, we find the difference is less than 0.7 mm. By checking the difference between different varieties of rice, such accuracy is good enough to discriminate between rice varieties (Zhang et al., 2002). The above experiments indicate that the 3D reconstruction method is a promising method for parameter measurements of phytoliths.
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
6. Conclusions In this article, we propose the use of extended depth of focus images in phytolith analysis with the optical microscope. The extended depth of focus image is generated from a series of photos taken under the optical microscope. The extended depth of focus image is sharp throughout, while the original photo usually has some portions out of focus. Thus, the extended depth of focus image is suitable for computer-assisted morphometrics analysis. Furthermore, we can reconstruct three-dimensional images from the extended depth of focus image. Experiments on rice husk multi-cell panels show that the three-dimension image reconstruction method can reconstruct multiple double-peaked phytoliths at one time. Initial experiments indicate that the three-dimensional image reconstruction method is promising in reducing the subjectivity of phytolith identification and improving the efficiency of parameter measurement. Acknowledgements This study was supported by grants from the National Science Foundation for Post-doctoral Scientists of China (20080440551) and the Knowledge Innovative Project (KJCX3.SYW.N12). References Ball, T.B., Gardner, J.S., Anderson, N., 1999. Identifying inflorescence phytoliths from selected species of wheat (Triticum monococcum, T. dicoccon, T. dicoccoides, and T. aestivum) and barley (Hordeum vulgare and H. spontaneum (Gramineae). American Journal of Botany 86 (11), 1615–1623 (doi:10.2307/2656798). Ball, T.B., Gardner, J.S., Brotherson, J.D., 1996. Identifying phytoliths produced by the inflorescence bracts of three species of wheat (Triticum monoccocum L., T. dicoccon Schrank., and T. aestivum L.) using computer-assisted image and statistical analysis. Journal of Archaeological Science 23, 619–632. Ball, T.B., Brotherson, J.D., 1992. The effect of varying environmental conditions on phytolith morphometries in two species of grass (Bouteloua curtipendula and Panicum virgatum). Scanning Electron Microscopy 6, 1163–1182. Bowdery, D., Hart, D.M., Lentfer, C., Wallis, L., 2001. A universal phytolith key. In: Meunier, J.D., Colin, F. (Eds.), Applications in Earth Sciences and Human History. Balkema, Amsterdam, pp. 267–278. Phytoliths.
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Forster, B., Van De, Ville D., Berent, J., Sage, D., Unser, M., 2004. Extended depth-offocus for multi-channel microscopy images: a complex wavelet approach. Proceedings of the Second IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’04), 660–663. Arlington VA, USA, April 15–18. Horrocks, M., Deng, Y., Ogden, J., Sutton, D., 2000. A reconstruction of the history of a Holocene sand dune on Great Barrier Island, northern New Zealand, using pollen and phytolith analyses. Journal of Biogeography 27, 1269–1277. Pearsall, D.M., 1978. Phytolith analysis of archeological soils: evidence for maize cultivation in formative Ecuador. Science 199, 177–178. Pearsall, D.M., 2000. Paleoethnobotany: A Handbook of Procedures, second ed. Academic Press, Inc., San Diego. Pearsall, D.M., 2002. Maize is still ancient in prehistoric Ecuador: the view from Real Alto, with comments on Staller and Thompson. Journal of Archaeological Science 29, 51–55. Pearsall, D.M., Piperno, D.R., Dinan, E.H., Umlauf, R., Zhao, Z.J., Benfer, R.A., 1995. Distinguishing rice (Oryza sativa Poaceae) from wild Oryza species through phytoliths analysis – results of preliminary research. Economic Botany 49, 183– 196. Piperno, D.R., 1984. A comparison and differentiation of phytoliths from maize (Zea mays L.) and wild grasses: use of morphological criteria. American Antiquity 49, 361–383. Piperno, D.R., 1988. Phytolith Analysis: An Archaeological and Geological Perspective. Academic Press, San Diego. Piperno, D.R., 2006. Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists. Alta Mira Press, Lanham, MD, p. 238. Rosen, A.M., 1992. Preliminary identification of silica skeletons from Near Easter archaeological sites: an anatomical approach. In: Rapp Jr., G., Mulholland, S.C. (Eds.), Phytolith Systematics: Advances in Archaeological and Museum Science. Plenum Press, New York, pp. 129–147. Rovner, I., Russ, J.C., 1992. Darwin and design in phytolith systematics: morphometric methods for mitigating redundancy. In: Rapp, G., Mulholland, S.C. (Eds.), Phytolith Systematics. Plenum Press, New York, pp. 253–276. Russ, J.C., Rovner, I., 1987. Stereological verification of Zea phytolith taxonomy. Phytolitharien Newsletter 4, 10–18. Twiss, P.C., 1987. Grass-Opal phytoliths as climatic indicators of the Great Plains Pleistocene, in: Johnson, W.C. (Ed.) Quaternary Environments of Kansas, fifth ed. pp. 179–188. Valdecasas, A.G., Marshall, D., Becerra, J.M., Terrero, J.J., 2001. On the extended depth of focus algorithms for bright field microscopy. Micron 32, 559–569. Valdecasas, A.G., Marshall, D., Becerra, J.M., 2002. extended depth-of-focus algorithms in brightfield microscopy. European Microscopy and Analysis 79, 15–17. Zhang, W., 2002. The bi-peak tubercle of rice, the character of ancient rice and the origin of cultivated rice. In: Yasuda, Y. (Ed.), The Origins of Pottery and Agriculture. Lustre Press and Roli Books, New Delhi, pp. 205–216. Zhao, Z., et al., 1998. Distinguishing rice (Oryza Sativa Poaceae) from wild Oryza species through phytolith analysis: final method. Economic Botany 52 (2), 134– 145.
Contents lists available at ScienceDirect
Journal of Archaeological Science journal homepage: http://www.elsevier.com/locate/jas
Extended depth of focus image for phytolith analysis Yan Wu*, Changsui Wang Department of Scientific History and Archaeometry, Graduate School of Chinese Academy of Sciences, Beijing, 100049, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 January 2009 Received in revised form 7 June 2009 Accepted 14 June 2009
This paper proposes a new phytolith analysis method by extended depth of focus technology. The extended depth of focus image is sharp throughout by the image processing of a series of photos, while each acquisition of the original photo will be compromised and show certain parts of the specimen in and out of focus. Consequently, the extended depth of focus image is suitable for computer-assisted morphometrics analysis. Meanwhile, the extended depth of focus image can be used for three-dimensional reconstruction images. Experiments on rice husk multi-cell panel show that the three-dimensional reconstruction image method can reconstruct multiple double-peaked phytoliths at one time. We also measure the parameters of multiple double-peaked phytoliths from the reconstructed threedimension image. Comparative experiments show that the reconstructed three-dimensional image is precise enough for rice identification. Based on the above experiments, we recommend the extended depth of focus method as a promising tool in phytolith research. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Archaeology Phytolith Extended depth of focus Image analysis
1. Introduction Phytoliths are opaline silica within and between cells formed in living plants by monosilicic acid brought into the plant through the uptake of water. Since phytoliths can create replicas of the plant cell bodies, phytoliths can be used to identify a certain genus or species according to its shape, size, ornaments, cell orientation and other anatomical features (e.g., Twiss, 1987; Piperno, 1988). Since the 1970s, phytolith analysis has been a fast developing discipline in the field of archaeology. They serve as efficient clues to paleoenvironmental reconstruction, agricultural activities and human activities through analysis and interpretation of phytolith assemblages from various sediments (e.g., Ball, 1999; Bowery, 2001; Horrocks, 2000; Pearsall, 2000, 2002; Piperno, 1988, 2006; Rosen, 1992). Most initial phytolith analysis attempts are based on the typological approach, where the frequency of types and/or shapes of phytoliths were used as classification criteria. However, a purely typologic approach is rarely enough to distinguish between grass taxa at the species level (Ball and Brotherson, 1992; Rovner and Russ, 1992). Morphometrics (measurements of size and shape) were proved to be useful in conjunction with typologies to improve the phytolith taxonomic resolution. The morphometric parameters can be directly measured by an eyepiece micrometer in the microscope (Pearsall, 1978; Piperno, 1984). Many researchers then used computer-assisted image analysis to acquire the morphometric parameters (Russ and Rovner, 1987; Ball, 1996). Because the * Corresponding author. Tel.: þ86 15810231703. E-mail address: [email protected] (Y. Wu). 0305-4403/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jas.2009.06.010
same phytolith may have two different shapes when viewed from different angles, making identification subjective and somewhat tricky (Piperno, 1988), there can be potential problems using 2D micrographs for typological analysis and morphometrics measurement. Thus, the authors propose to reconstruct a threedimensional image from a pair of two dimension photos taken under scan electron microscope (e.g., Wu, 2006). Most reported work using computer-assisted image analysis is based on scanning electron micrographs. However, the scanning electron microscope is expensive and difficult to operate, which limits its application in daily research. In comparison, the optical microscope is much cheaper and easier to operate. Image analysis based on the optical microscope will have broader applications. However, one old and familiar problem of optical microscopes is the limited depth of focus. In this paper, we propose to use extended depth of focus (EDF) technology for phytolith analysis. The EDF image can be used for statistical analysis. Meanwhile, a three-dimensional reconstruction image can be reconstructed from the EDF image, which can be used for morphological analysis and parameter measuring. Experiments show that the three-dimension image reconstruction method is promising in reducing the subjectivity of phytolith identification and improving the efficiency of measurement.
2. Extended depth of focus principle and application A lack of depth of focus in microscope images is a common problem in biological imaging with conventional optical microscopy.
2254
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
Fig. 3. Original image taken under optical microscope.
Fig. 1. 3D reconstruction principle from optical microscope.
Fig. 2. Original image taken under optical microscope.
When the specimen surface is irregular, portions of the object’s surface outside the focal plane will appear defocused. Consequently, each acquired image can be composed of certain parts of the specimen that are in and out of focus. To reconstruct an image that is sharp everywhere, multiple images are taken at different focal planes. The challenge then becomes to select from each slice the area that is focused in order to reconstruct an image that is sharp everywhere (Manjunath, 1995; Valdecasas, 2001, 2002; Forster, 2004). In this way, it is possible to extend the depth of focus without the physical limitation of the numerical aperture of the objective lens. One of the known methods is based on the wavelet transform (Forster, 2004). Multiple microscope systems provide EDF image generation function and corresponding 3D reconstruction function by EDF technology, such as the Soft Imaging System by the Gmb H Company and the VE-8800 Microscope system by the Keyence Company. In the Soft Imaging System by the Gmb H Company, the systems first capture a focus image series with an even interval of focus. Then, the system goes through the whole image series pixel by pixel (to be more precise, EDF goes through all the groupings of pixels),
Fig. 4. Extended depth of focus image.
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
2255
Fig. 5. Extended depth of focus image of common millet phytolith.
and determines which image contains the pixel(s) that is most focused (for each particular image position) out of all the images of the series. Each image is assigned a specific numerical value, which represents a gray value and is entered as such into a gray-value image at the respective image location. Upon completion of this algorithm a complete gray-value image will be ready to describe which image positions/pixel groupings from which images of the series are to be included in the (infinitely) sharp EDF image. Meanwhile, the selected areas of each individual image indicate in which height range the area of the specimen surface is. This is why the gray-value image is
also a qualitative height map of the specimen surface. Thus we can reconstruct the 3D image from this gray-value image, as shown in Fig. 1. In our practice, to acquire images series for 3D reconstruction, we usually put the most interested area in the middle and observe from the top-down angle.
Fig. 6. EDF image.
Fig. 7. Reconstructed 3D image.
3. Extended depth of focus image for morphometrics analysis When the phytolith surface is irregular, portions of the object’s surface outside the focal plane will appear defocused. It is not easy to make a statistical analysis of a phytolith with the original image
2256
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257 Table 1 Comparison of measured values.
Fig. 8. Reconstructed 3D image.
taken under the optical microscope, as shown in Figs. 2 and 3. Here, we generate an extended depth of focus image generated from 21 original images with even intervals with the Soft Imaging System (Gmb H Company), as shown in Fig. 4. Obviously, it is easier and more accurate to make a statistical analysis with the extended depth of focus image. Furthermore, the extended depth of focus image can also be archived for future examination and for education. Meanwhile, the EDF image can also be used to analyze the details of a single phytolith. Shown in Fig. 5a,b,c are three images taken under the optical microscope, while Fig. 5d is an extended depth of focus image. Fig. 5d is sharp everywhere while Fig. 5a,b,c all have some portions defocused. 4. Three-dimension reconstruction by extended depth of focus image We chose a rice husk multi-cell panel taken from the Shangzhou site as a sample for 3D reconstruction by EDF image. The doublepeak parameters of bi-peak-tubercle structure are important clues for rice identification. In many sites, we can easily find rice husk multi-cell panels. If we can measure the double-peak parameters in rice husk multi-cell panels, we can greatly improve the efficiency of rice identification. However, it is usually difficult to measure the double-peak parameters in rice husk multi-cell panels directly under the optical microscope because of sheltering by other double-peaks. Researchers often need to tap the slide and hope by good luck to get the right angle. Sometimes, because of unsatisfactory observation angles, researchers may make mistakes in shape identification and parameter measurements. In comparison,
Fig. 9. Reconstructed 3D image.
Measured values under microscope (mm)
Measured values in reconstructed 3D image (mm)
Difference (mm)
36.3 41.1 39.1 28.8 27.6 38.5 48.2 44.8 32.7 29.6 21.8 38.2 36.3 27.9 39.9 37.8 35.7 30.2 33.4 31.4
36.7 41.6 39.8 29.1 27.1 39.1 48.0 44.5 33.3 31.1 22.1 38.4 36.4 28.1 39.6 38.1 35.9 29.8 33.8 31.6
0.4 0.5 0.7 0.3 0.5 0.6 0.2 0.3 0.6 0.5 0.3 0.2 0.1 0.2 0.3 0.3 0.2 0.4 0.4 0.2
it is much more efficient to rotate and shift the reconstructed 3D image to make identifications and measurements. We generated the EDF image and 3D image from 23 original images with the Keyence VE-8800 microscope system. It is well known that the rice husk multi-cell panel originates from the bipeak-tubercle structure of rice. Fig. 6 is the generated EDF image. Figs. 7, 8 and 9 are the reconstructed three-dimensional images of the sample. The graph shows that the three-dimension image reconstruction method can reconstruct multiple peaks at one time. We can also focus on measuring one double-peak by enlarging, rotating and shifting to make more precise judgments and measurements.
5. Initial accuracy analysis In order to verify the precision of parameters measured from a reconstructed 3D image, it is necessary to compare them with the parameters measured directly under an optical microscope. To distinguish ‘domesticated’ rice from ‘wild’ rice is very important for studying the origins and migration of rice. The specific shapes of the phytoliths in rice that distinguish ‘domesticated’ from ‘wild’ are the double-peaked glume cell phytoliths (Zhao et al., 1998). In Zhao’s paper, five measurements were taken for each individual glume cell using an eyepiece micrometer, including width of the top (TW), width of the middle (MW), the height of each peak (H1 and H2) and the depth of the curve (CD). A derived formula based on these five measurements permits correct classification of domesticated rice glume cells in 81.6% of the test cases (Pearsall et al., 1995). Because the top width of double-peaked papilla can be relatively easily measured under the optical microscope, we used this parameter for comparative analysis. Table 1 lists the top widths directly measured under the optical microscope in column 1 and those measured in reconstructed 3D image in column 2. In this comparison, measurement in reconstructed 3D image is accomplished with the VE-8800 microscope system by the Keyence Company. Their differences are listed in column 3. From Table 1, we find the difference is less than 0.7 mm. By checking the difference between different varieties of rice, such accuracy is good enough to discriminate between rice varieties (Zhang et al., 2002). The above experiments indicate that the 3D reconstruction method is a promising method for parameter measurements of phytoliths.
Y. Wu, C. Wang / Journal of Archaeological Science 36 (2009) 2253–2257
6. Conclusions In this article, we propose the use of extended depth of focus images in phytolith analysis with the optical microscope. The extended depth of focus image is generated from a series of photos taken under the optical microscope. The extended depth of focus image is sharp throughout, while the original photo usually has some portions out of focus. Thus, the extended depth of focus image is suitable for computer-assisted morphometrics analysis. Furthermore, we can reconstruct three-dimensional images from the extended depth of focus image. Experiments on rice husk multi-cell panels show that the three-dimension image reconstruction method can reconstruct multiple double-peaked phytoliths at one time. Initial experiments indicate that the three-dimensional image reconstruction method is promising in reducing the subjectivity of phytolith identification and improving the efficiency of parameter measurement. Acknowledgements This study was supported by grants from the National Science Foundation for Post-doctoral Scientists of China (20080440551) and the Knowledge Innovative Project (KJCX3.SYW.N12). References Ball, T.B., Gardner, J.S., Anderson, N., 1999. Identifying inflorescence phytoliths from selected species of wheat (Triticum monococcum, T. dicoccon, T. dicoccoides, and T. aestivum) and barley (Hordeum vulgare and H. spontaneum (Gramineae). American Journal of Botany 86 (11), 1615–1623 (doi:10.2307/2656798). Ball, T.B., Gardner, J.S., Brotherson, J.D., 1996. Identifying phytoliths produced by the inflorescence bracts of three species of wheat (Triticum monoccocum L., T. dicoccon Schrank., and T. aestivum L.) using computer-assisted image and statistical analysis. Journal of Archaeological Science 23, 619–632. Ball, T.B., Brotherson, J.D., 1992. The effect of varying environmental conditions on phytolith morphometries in two species of grass (Bouteloua curtipendula and Panicum virgatum). Scanning Electron Microscopy 6, 1163–1182. Bowdery, D., Hart, D.M., Lentfer, C., Wallis, L., 2001. A universal phytolith key. In: Meunier, J.D., Colin, F. (Eds.), Applications in Earth Sciences and Human History. Balkema, Amsterdam, pp. 267–278. Phytoliths.
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