Estimation of Nano-Pore Size Using Image Processing
DOI:
https://doi.org/10.21928/uhdjst.v1n1y2017.pp38-44Keywords:
Feature Extraction, Image Segmentation, Nanopore, Segmentation EvaluationAbstract
Nanopores, which are nanometer-sized holes, have been utilized in apparatus that point toward sensing a range of molecules such as DNA and RNA and single proteins The important factor for sensing molecules is diameters of nanopores which can be found through a substantial process called segmenting for nanopores of scanning electron microscope (SEM) images. In this investigation, four segmentation methods, namely, threshold, bilateral filter, k-means, and expectation maximization - Gaussian mixture model (EM-GMM) which has been utilized to segment three SEM images of nanopores efficiently. The quality of segmentation evaluated objectively through computing Rand index among them. Consequently, the nanopore size of Al2O3 films computed by means of SEM images. This study found that EM-GMM segmenting method gives promising results among other examined methods. It is for their high R-index, minimum adjustment parameters (just one variable which set usually 2), and low consuming time. Hence, it can be used efficiently for computing nanopore count and size.
Index Terms: Feature Extraction, Image Segmentation, Nanopore, Segmentation Evaluation
References
[2] M. Ahmed, S. Abd El-attySoliman and J. Adamkani. “Performance study of innovative and advanced image segmentation techniques.” Singaporean Journal of Scientific Research, vol. 7, no. 12015, pp. 320-326, 2014.
[3] M.Akhtaruzzaman,A.A. Shafie and R. Khan. “Automated threshold detection for object segmentation in colour image. ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 6, pp. 4100- 4104, 2016.
[4] C. Nguyen, J. Havlicek, Q. Duong, S. Vesely, R. Gress, L. Lindenberg, P. Choyke, J. H. Chakrabarty and K. Williamset. “An automatic 3D CT/PET segmentation framework for bone marrow proliferation assessment.” 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, 2016, pp. 4126-4130.
[5] A. S. Sahadevan, A. Routray, B. S. Das and S. Ahmad. “Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines.” Journal of Applied Remote Sensing, vol. 10, no. 2, pp. 025004, Apr. 2016.
[6] Y. Y. Chen, W. S. Chen and H. S. Ni. “Image segmentation in thermal images.” 2016 IEEE International Conference on Industrial Technology (ICIT), Taipei, 2016, pp. 1507-1512.
[7] Z. Fu and L. Wang. “Color image segmentation using gaussian mixture.” in Multimedia and Signal Processing: Second International Conference, CMSP 2012, Shanghai, China, 2012.
[8] K. Kalti and M. Mahjoub. “Image segmentation by Gaussian mixture models and modified FCM algorithm.” The International Arab Journal of Information Technology, vol. 11, no. 1, pp. 11-18, 2014.
[9] S. Alexander, R. Azencott, B. Bodmann, A. Bouamrani, C. Chiappini, M. Ferrari, X. Liu and E. Tasciotti. “SEM image analysis for quality control of nanoparticles.” in Computer Analysis of Images and Patterns, Springer, Berlin, Heidelberg, 2009. pp. 590-597.
[10] U. Phromsuwan, Y. Sirisathitkul, C. Sirisathitkul, P. Muneesawang and B. Uyyanonvara. “Quantitative analysis of X-ray lithographic pores by SEM image processing.” MAPAN-Journal of Metrology Society of India, vol. 28, no. 4, pp. 327-333, 2013.
[11] P. Bannigidad and C. Vidyasagar. “Effect of time on anodized Al2O3 nanopore FESEM images using digital image processing techniques: A study on computational chemistry.” International Journal of Emerging Trends and Technology in Computer Science (IJETTCS), vol. 4, no. 3, pp. 15-22, 2015.
[12] C. Vidyasagar, P. Bannigidad and H. Muralidhara. “Influence of anodizing time on porosity of nanopore structures grown on flexible TLC aluminium films and analysis of images using MATLAB software.” Advanced Materials Letters, vol. 7, no. 1, pp. 71-77, 2016.
[13] G. Macias, J. Ferré-Borrull, J. Pallarès and L. Marsal. “Effect of pore diameter in nanoporous anodic alumina optical biosensors.” The Analyst, vol. 140, no. 14, pp. 4848-4854, 2015.
[14] M. H. J. Vala and A. Baxi. “A review on Otsu image segmentation algorithm.” International Journal of Advanced Research in Computer Engineering and Technology, vol. 2, no. 2, pp. 387-389, 2013.
[15] N. Dhanachandra, K. Manglem and Y. Chanu. “Image segmentation using K-means clustering algorithm and subtractive clustering algorithm.” Procedia Computer Science, vol. 54, pp. 764-771, 2015.
[16] S. Agarwal and P. Kumar. “Denoising of a mixed noise color image through special filter.” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 1, pp. 159-176, 2016.
[17] T. Xiong, L. Zhang and Z. Yi. “Double Gaussian mixture model for image segmentation with spatial relationships.” Journal of Visual Communication and Image Representation, vol. 34, pp. 135-145, 2016.
[18] R. Unnikrishnan and M. Hebert. “Measures of similarity.” Application of Computer Vision, 2005. WACV/MOTIONS ‘05. vol. 1. Seventh IEEE Workshops on, Breckenridge, CO., 2005, pp. 394.
