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A novel approach for marker-controlled watershed segmentation based on morphological filters is proposed to obtain the statistic data of quantum dot from atomic force microscopy photos

Automatic Morphological Measurement of the Quantum Dots Based on Marker-Controlled Watershed Algorithm

IEEE Transactions on Nanotechnology, no. 1 (2013): 51-56

Cited: 14|Views45
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Abstract

In the field of material growth, the quantum dot (QD) image analysis is a fundamental tool. The main challenge is that such study is used to be made by nonautomatic procedures which are time consuming and subjective. We aim to implement an algorithm of automatic analysis of the QDs images. In this frame, efficient QDs segmentation is prer...More

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Introduction
  • T HE quantum dot (QD) is a nanostructure whose excitons and carriers are confined in all the three spatial dimensions to a nanometer-sized zone [1].
  • QDs are of great interest to physicists for both fundamental and technological motivations [1]
  • Their atomic properties enable them good supporters for studying the physics of confined excisions and carriers, which attract considerable theory interest.
  • The background marker extraction can be achieved by computing the watershed transform of the distance transform of the foreground marker image.
  • In 2-D space, an image can be regarded to consist of two classes of pixels: object and background
  • The former is regarded as the light point while the latter black [5].
  • For connected convex objects, such as the QDs, the geographical distance is equivalent to the Euclidean distance [3]
Highlights
  • T HE quantum dot (QD) is a nanostructure whose excitons and carriers are confined in all the three spatial dimensions to a nanometer-sized zone [1]
  • In order to overcome the oversegmentation, in this paper, we propose the marker-controlled watershed which only allows local minima to occur inside the markers created by applying an opening-by-reconstruction and closing-by-reconstruction morphological filter to the original QDs images, followed by marking foreground objects and background locations
  • A novel approach for marker-controlled watershed segmentation based on morphological filters is proposed to obtain the statistic data of QDs from atomic force microscopy photos
  • The proposed method is based on the presumption that there exists one-to-one correspondence between the markers and the QDs
  • When several QDs converge in one cluster, the algorithm would fail to detect the correct number of QDs, which affects the statistical values
  • We should emphasize that the experimental results strongly contribute to solve the crucial problem of the automatic detection and statistics of the QDs
Results
  • The input image from atomic force microscopy photos is 1 × 1 μm2, corresponding to 512 × 512 pixels.
  • The length of one pixel equals about 1.95 × 1.95 nm2.
  • Fig. 3(a) shows an experimental sample.
  • The authors implement the proposed method in the MATLAB programming environment and run it on a PC with Intel Core2 2.4GHZ CPU.
  • Fig. 3(b)–(f) shows the
Conclusion
  • A novel approach for marker-controlled watershed segmentation based on morphological filters is proposed to obtain the statistic data of QDs from atomic force microscopy photos.
  • Based on the marker-controlled watershed algorithm, the procedure is very fast and robust
  • It segments the QDs with a small average error.
  • The proposed method generated very few undersegmentation of QDs when the dividing QDs are ambiguous to separate due to the low contrast.
  • Perhaps, these are the limits of the automation, or the authors need more advanced approaches to separate the clustered QDs automatically.
Related work
  • In the process of the QDs morphological analysis, the QDs segmentation is the prerequisite. When there is a high contrast between the QDs and the background, the segmentation can be achieved by the method based on global thresholding [8], [9]. However, this approach fails to detect the overlapping QDs for the spatial relations are not embedded in basic thresholding techniques [10]. Considering the low contrast of the QDs image and the touched QDs, the segmentation method based on mathematical morphology is adopted. As a nonlinear method of signal processing, mathematical morphology is proved to be a powerful tool to facilitate image analysis and image segmentation [11], remaining the basic shape characteristic of the QDs. Based on mathematical morphology, the watershed algorithm is a promising method.
Funding
  • This work was supported by the Natural Science Foundation of China under Grant 61076014 and the “Strategic Priority Research Program” of the Chinese Academy of Sciences under Grant XDA06020700
Reference
  • P. Belardinelli, S. Capoleoni, B. Tirozzi, and C. Coluzza, “Application of a segmentation algorithm to quantum dots study,” J. Vac. Sci. Technol. B, vol. 22, no. 2, pp. 588–591, 2004.
    Google ScholarLocate open access versionFindings
  • D. Ding, “Analysis of AFM measurement on quantum dots,” in Proc. 3rd China-Sweden Meeting Nanometer-Scale Sci. Technol., Beijing, 2001, pp. 22–25.
    Google ScholarLocate open access versionFindings
  • J. Cheng and J. C. Rajapakse, “Segmentation of clustered nuclei with shape markers and marking function,” IEEE Trans. Biomed. Eng., vol. 56, no. 3, pp. 741–748, Mar. 2009.
    Google ScholarLocate open access versionFindings
  • O. Debeir, I. Adanja, N. Warzee, P. Van Ham, and C. Decaestecker, “Phase contrast image segmentation by weak watershed transform assembly,” in Proc. 5th IEEE Int. Symp. Biomed. Imaging: From Nano to Macro, 2008, pp. 724–727.
    Google ScholarLocate open access versionFindings
  • Y. Zhao, J. Liu, H. Li, and G. Li, “Improved watershed algorithm for dowels image segmentation,” in Proc. 7th World Congr. Intell. Control Autom., Chongqing, China, 2008, pp. 7644–7648.
    Google ScholarLocate open access versionFindings
  • J. Feng, L. Huaxiang, L. Kai, C. Yonghai, and W. Zhanguo, “Mathematical morphology based algorithm to measure quantum dots from AFM photos,” Chin. J. Semicond., vol. 26, no. 1, pp. 2120–2126, Nov. 2005.
    Google ScholarLocate open access versionFindings
  • N. H. Salman and L. Chongqing, “Watershed-based image segmentation with region merging and edge detection,” High Technol. Lett., vol. 9, no. 1, pp. 58–63, 2003.
    Google ScholarLocate open access versionFindings
  • T. R. Jones, A. Carpenter, and P. Golland, “Voronoi-based segmentation of cells on image manifolds,” in Proc. ICCV Workshop Comput. Vision Biomed. Image Appl., 2005, pp. 535–543.
    Google ScholarLocate open access versionFindings
  • G. Xiong, X. Zhou, L. Ji, P. Bradley, N. Perrimon, and S. Wong, “Segmentation of drosophila RNAI fluorescence images using level sets,” in Proc. IEEE Int. Conf. Image Process., 2006, pp. 73–76.
    Google ScholarLocate open access versionFindings
  • M. M. F. A. Niraimathi and V. Seenivasagam, “A fast fuzzy-C means based marker controlled watershed segmentation of clustered nuclei,” in Proc. Int. Conf. Comput. Commun. Electr. Technol., 2011, pp. 186–192.
    Google ScholarLocate open access versionFindings
  • J. Serra, Image Analysis and Mathematical Morphology, Orlando, FL: Academic, 1982.
    Google ScholarFindings
  • L. Vincent and Pierre Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 6, pp. 583–598, Jun. 1991.
    Google ScholarLocate open access versionFindings
  • J. B. T. M. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies,” Fundamental Informaticae, vol. 41, pp. 187–228, 2001.
    Google ScholarLocate open access versionFindings
  • H. Y. Wang and C. W. Yuan, “License plate location method based on integrated features and marker-controlled watershed algorithm,” in Proc. Int. Conf. Electr. Control Eng., 2010, pp. 1234–1237.
    Google ScholarLocate open access versionFindings
  • F. Meyer and S. Beucher, “Morphological segmentation,” J. Visual Commun. Image Represent., vol. 1, no. 1, pp. 21–46, Sep. 1990.
    Google ScholarLocate open access versionFindings
  • K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging,” IEEE Trans. Image Process., vol. 2, pp. 1684–1698, Dec. 1998.
    Google ScholarLocate open access versionFindings
  • X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using markercontrolled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 53, no. 11, pp. 2405–2414, Nov. 2006.
    Google ScholarLocate open access versionFindings
  • L. Vincent, “Morphological gray scale reconstruction in image analysis: Applications and efficient algorithms,” IEEE Trans. Image Process., vol. 2, no. 2, pp. 176–201, Apr. 1993.
    Google ScholarLocate open access versionFindings
  • R. C. Gonzalez and R. E. Woods, Digital Image Processing (Second Edition). Englewood Cliffs, NJ: Prentice Hall, 2002. Lulu Xu received the B.S. degree in Honor Program of Electronic Engineering from China Agricultural University, Beijing, China, in 2010, and is currently working toward the Ph.D. degree in the Institute of Semiconductors, Chinese Academy of Sciences, Beijing.
    Google ScholarLocate open access versionFindings
  • Huaxiang Lu received the B.S. degree in information and electronic engineering from Zhejiang University, Zhejiang, China, in 1985, and the M.S. and Ph.D. degrees in microelectronics both from the Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China, in 1988 and 1993, respectively.
    Google ScholarLocate open access versionFindings
  • Since 1997, he has been a Researcher Scientist in the Chinese Academy of Sciences. He is the Committee Director of the Chinese Association for Artificial Intelligence in Neural Network and Computational Intelligence. His current research interests include the circuit and system, the semiconductor artificial neural network, and information and signal processing.
    Google ScholarFindings
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