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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Matematika Sains dan Teknologi Jurnal Pengabdian Magister Pendidikan IPA Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters Medika Respati : Jurnal Ilmiah Kesehatan
Khusnul Ain
Department Of Physics, Faculty Of Science And Technology, Universitas Airlangga
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering Arfan Eko Fahrudin; Endarko Endarko; Khusnul Ain; Agus Rubiyanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3987-3997

Abstract

Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.
Co-Authors Abidah Alfi Maritsa Agus Rubiyanto Alfian Pramudita Putra Amillia Kartika Sari Amirul Amalia Arfan Eko Fahrudin Bambang Haris Betty Rahayuingsih David Buntoro Kamandjaja Deddy Kurniadi Dewi Masykuriyah Endarko Endarko Febriana Dwi Rahmadani Hanif Assyarify Iis Purwanti Imam Sapuan Khouliya Zalda Lailatul Muqmiroh Limajatini, Limajatini Lina Choridah Miranda Syafira Widyananda Muhammad Fadhel Maulama Nafisa Rahmatul Laili Alami Nita Lutfiyah Osmalina Nur Rahma Pramulen, Aji Sapta R Arif Wibowo Rifai Chai Riries Rulaningtyas Soegianto Soelistiono Solly Aryza Suprayitno Suprayitno Suryani Dyah Astuti Weni Endahing Warni Yusrinourdi Muhammad Zuchruf