International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 5, Issue 3 (March 2018), Pages: 1-7

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 Original Research Paper

 Title: Development of an automated multi-thresholding technique for identification of different materials types and concentration using CT scans

 Author(s): Lay Yean Ng 1, *, Moayyad Al Ssabbagh 2, Abd Aziz Tajuddin 1, 2, Ibrahim Lutfi Shuaib 1, Rafidah Zainon 1

 Affiliation(s):

 1Advanced Medical and Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia
 2School of Physics, Universiti Sains Malaysia, Pulau Pinang, Malaysia

 https://doi.org/10.21833/ijaas.2018.03.001

 Full Text - PDF          XML

 Abstract:

The aim of this study is to evaluate the efficacy of a new automated quantification technique in differentiating different types of materials using the single-energy and dual-energy computed tomography (CT). Five different concentrations of calcium chloride, and two different concentrations of iron (III) nitrate and sunflower oil were used. The eight solutions were placed into a PMMA container, which was filled with water and scanned using a single-source dual energy CT that is capable of producing single and dual-energy images. Five energies (70, 80,100, 20 and 140 kVp) were applied to produce the single-energy images, while only two energies with high (140 Kvp) and low voltages (80 kVp) were used to produce the fused CT images. The pitch and slice thickness selected were 0.6 mm and 1 mm respectively. The developed image processing software was used to evaluate the CT numbers of each type of solution. The results were compared with the Weasis software v1.2.7 and Somaris7/Syngro CT2012B to verify the new algorithm. The developed automatic multi-thresholding algorithm can differentiate solutions with high concentration from different type of materials. The mean percentage differences of CT numbers obtained from new algorithm, Weasis software and Syngro software show no significant difference. The developed multi-thresholding algorithm was unable to distinguish between low concentration solutions. However, it had the ability to detect the solutions with high concentration from each material. The developed image processing software based on multi-thresholding method showed promising results to detect different type of materials with various concentrations, which can aid in detecting different types of plaques in coronary heart disease. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Dual-energy, Single-energy, Algorithm, Thresholding, Concentrations

 Article History: Received 17 January 2017, Received in revised form 27 November 2017, Accepted 18 December 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.03.001

 Citation:

 Ng LY, Ssabbagh MA, Tajuddin AA et al. (2018). Development of an automated multi-thresholding technique for identification of different materials types and concentration using CT scans. International Journal of Advanced and Applied Sciences, 5(3): 1-7

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I3/Ng.html

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 References (15)

  1. Carr JJ, Nelson JC, Wong ND, McNitt-Gray M, Arad Y, Jacobs JDR, Sidney S, Bild DE, Williams OD, and Detrano RC (2005). Calcified coronary artery plaque measurement with cardiac CT in population-based studies: Standardized protocol of multi-ethnic study of atherosclerosis (MESA) and coronary artery risk development in young adults (CARDIA) study. Radiology, 234(1): 35-43. https://doi.org/10.1148/radiol.2341040439  PMid:15618373 
  2. Demirkaya, O, M H Asyali, and P K Sahoo (2008). Image processing with MATLAB: Applications in medicine and biology. CRC Press, Ney York, USA.     
  3. Gonzales RC, Woods RE, and Eddins SL (2006). Digital image processing using MATLAB [Russian translation]. Google Scholar, Tekhnosfera, Moscow: 206-253.     
  4. Gutman DA, Dunn JWD, Cobb J, Stoner RM, Kalpathy-Cramer J, and Erickson B (2014). Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer. Frontiers in Neuroinformatics, 8: Article 53. https://doi.org/10.3389/fninf.2014.00053 
  5. Henzler T, Porubsky S, Kayed H, Harder N, Krissak UR, Meyer M, Sueselbeck T, Marx A, Michaely H, Schoepf UJ, and Schoenberg SO (2011). Attenuation-based characterization of coronary atherosclerotic plaque: Comparison of dual source and dual energy CT with single-source CT and histopathology. European Journal of Radiology, 80(1): 54-59. https://doi.org/10.1016/j.ejrad.2010.07.024  PMid:20810229 
  6. Hong C, Bae KT, and Pilgram TK (2003). Coronary artery calcium: Accuracy and reproducibility of measurements with Multi–Detector Row CT—assessment of effects of different thresholds and quantification methods. Radiology, 227(3): 795-801. https://doi.org/10.1148/radiol.2273020369  PMid:12728184     
  7. Kamath S, Song W, Chvetsov A, Ozawa S, Lu H, Samant S, Liu C, Li JG, and Palta JR (2011). An image quality comparison study between XVI and OBI CBCT systems. Journal of Applied Clinical Medical Physics, 12(2): 376-390. https://doi.org/10.1120/jacmp.v12i2.3435 
  8. Kamdi S and Krishna RK (2012). Image segmentation and region growing Aalgorithm. International Journal of Computer Technology and Electronics Engineering 2(1): 103–107.     
  9. Kristanto W, Van Ooijen PM, Groen JM, Vliegenthart R, and Oudkerk M (2012). Small calcified coronary atherosclerotic plaque simulation model: Minimal size and attenuation detectable by 64-MDCT and MicroCT. The International Journal of Cardiovascular Imaging, 28(4): 843-853. https://doi.org/10.1007/s10554-011-9869-3  PMid:21509430 PMCid:PMC3360866 
  10. Kristanto W, Van Ooijen PM, Jansen-van der WMC, Vliegenthart R, and Oudkerk M (2013). A meta-analysis and hierarchical classification of HU-based atherosclerotic plaque characterization criteria. PLoS ONE, 8(9): 1–13. https://doi.org/10.1371/journal.pone.0073460  PMid:24019924 PMCid:PMC3760884     
  11. Liu X, Yu L, Primak AN, and McCollough CH (2009). Quantitative imaging of element composition and mass fraction using dual‐energy CT: Three‐material decomposition. Medical Physics, 36(5): 1602-1609. https://doi.org/10.1118/1.3097632  PMid:19544776 PMCid:PMC2719492 
  12. Motoyama S, Sarai M, Harigaya H, Anno H, Inoue K, Hara T, Naruse H, Ishii J, Hishida H, Wong ND, and Virmani R (2009). Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. Journal of the American College of Cardiology, 54(1): 49-57. https://doi.org/10.1016/j.jacc.2009.02.068  PMid:19555840 
  13. Rais NB, Hanif MS, and Taj IA (2004). Adaptive thresholding technique for document image analysis. In the 8th International Multitopic Conference of INMIC, IEEE: 61-66. https://doi.org/10.1109/INMIC.2004.1492847 
  14. Sharma N and Aggarwal LM (2010). Automated medical image segmentation techniques. Journal of Medical Physics, 35(1): 3–14. https://doi.org/10.4103/0971-6203.58777 PMid:20177565 PMCid:PMC2825001     
  15. Yamak D (2013). Characterization of coronary atherosclerotic plaques by dual energy computed. Ph.D. Dissertation, Arizona State University, Arizona, USA.