The Digital Microscope and Its Image Processing Utility
Abstract
Many institutions, including high schools, own a large number of analog or ordinary microscopes. These microscopes are used to observe small objects. Unfortunately, object observations on the ordinary microscope require precision and visual acuity of the user. This paper discusses the development of a high-resolution digital microscope from an analog microscope, including the image processing utility, which allows the digital microscope users to capture, store and process the digital images of the object being observed. The proposed microscope is constructed from hardware components that can be easily found in Indonesia. The image processing software is capable of performing brightness adjustment, contrast enhancement, histogram equalization, scaling and cropping. The proposed digital microscope has a maximum magnification of 1600x, and image resolution can be varied from 320x240 pixels up to 2592x1944 pixels. The microscope was tested with various objects with a variety of magnification, and image processing was carried out on the image of the object. The results showed that the digital microscope and its image processing system were capable of enhancing the observed object and other operations in accordance with the user need. The digital microscope has eliminated the need for direct observation by human eye as with the traditional microscope.
Full Text:
PDFReferences
. Wada S, Ooki T, Tatsuya L, Nakanishi Y, Development of Dynamic Outdoor Education Program - Using Digital Microscope and Wireless LAN, 2009 Ninth IEEE International Conference on Advanced Learning Technologies. 2009: 281-282.
. Tohmyoh H, Takeda H, Akanda MAS. Evaluation of Mechanical and Electrical Properties of Very-Thin Pt Wires by Utilizing Joining Technique with Joule Heating. Journal Soc. Mater. Sci., 2009; 58:847-851.
. Hernandez L, Gothreaux P, Collins G, Shih L, and Campbell G, Digital Pathological Image Analysis and Cell Segmentation. Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference Workshops (CSBW’05), 2005.
. Patel VC, McClendon RW, Goodrum J.W. Detection of Blood Spots Dirt Stain in Eggs Using Computer Vision Neural Networks. Applied Enginering in Agriculture. 1996; 2(2): 253-258.
. Tishko DN, Tishko TV, Yu T, Zadneprovskiy A, Kuprin AS, Zgoda IV. Application of the digital holographic interference microscope for thin films investigation. OPT'2007. Kharkiv, Ukraine. 2007:70-71.
. Yu-Jen C, Yu-Sing Y, Ming-Shing Y, Yan-Chay L, Chen-Song C, Sun-Lon J, Ke-Nung H. The study of a handheld digital microscope for biomedical applications. IEEE. 2008; 978-1-4244-1748-3/08.
. Gonzalez RC, Woods P. Digital Image Processing, Third Edition, New Jersey: Prentice Hall, Inc. 2008.
. Jahne B. Digital Image Processing, Sixth Ed. Springer. 2005.
. Isnanto RR, Hidayanto A, Hadi MN. Identifikasi Sidik Jari Menggunakan Template Tapis Gabor. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2007; 5(1): 1-8.
. Hariyanto D. Studi Penentuan Nilai Resistor Menggunakan Seleksi Warna Model HSI Pada Citra 2D. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2009; 7(1): 13-22.
. Rao PS, Gopal A, Revathy R, Meenakshi K. Color Analysis of Fruits using Machine Vision System for Automatic Sorting and Grading, J. Instru. Soc. India, 2004; 34(4): 284-291.
. Haeruddin, Hartati S, Harjoko A. An anomaly Detection of Rontgent Image Based on Fuzzy Logic Image Enhancement. Teknosain. 2003; 16(2):179-188.
. Harjoko A, Hartati S, Elfizar. Motion Detection Using Optical Flow. Berkala Ilmiah MIPA. 2003; XII(3): C13-C19.
. Harjoko A, Hartati S, Trisnawan D. A Comparison Study of the Fourier Transform Based Algorithm, and the Artificial Neural Network Based Algorithm in Detecting Fabric Texture Defect. Proceedings of the International Conference on Mathematics and Its Applications, 2003; 12:400-404.
. Firdausy K, Sutikno T, Prasetyo E. Image Enhancement using Contrast Stretching on RGB and IHS Digital Image. TELKOMNIKA Indonesian Journal of Electrical Engineering 2007; 5(1): 45-50.
. Hartati S, Aklis I. Fuzzy Histrogram Hyperbolization-Based for Rontgen Image Anomaly Detection. Jurnal Teori Terapan Matematika. 2004; 4(1): 189-194.
. Hartati S, Nickerson B. Fuzzy hyperbolization image enhancement and artificial neural network for anomaly detection. Proceedings of the World Association of Science, Engineering and Technology. 2009; 56: 26-28.
. Maini R, Aggarwal H. A Comprehensive Review of Image Enhancement Techniques. Journal of Computing. 2010; 2(3): 11-16.
. Çatalyürek U, Beynon MD, Chang C, Kurc T, Sussman A, Saltz J. The Virtual Microscope. IEEE Trans. Information Technology in Bbiomedicine. 2003; 7(4): 230-248.
. Tai-Shan L, Feng-Chang H, Chien-Shing L, Kuo-Cheng H, Po-Jui C, Fong-Zhi C. The development of Portable digital microscope inspecting instrument. International Conference on Smart Manufacturing Application, Gyeonggi-do, Korea. 2008: 474-476.
. Anand A, Vijay RS, Qu W, Taslima K. Compact digital holographic microscopes and application. CLEO Pacific Rim. Shanghai, China, 2009.
. Gang H, Jinchuan L, Xuejin H, Yuanwen Z. An Integrated Auto-focusing System for Biomedical Digital Microscope. 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010). 2010: 1420-1423.
. Feng-Chang H, Chien Shing L, Kuo-Cheng H, Po-Jui C, Fong-Zhi C, Tai-Shan L. Portable digital microscope apparatus. Review of Scientific Instrument. 2006; 77: 116106-1-2.
DOI: http://doi.org/10.12928/telkomnika.v9i3.749
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604