Relevant bibliographies by topics / Image processing of Electrical impedance tomography

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Academic literature on the topic 'Image processing of Electrical impedance tomography'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Image processing of Electrical impedance tomography"

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Wang, Zeying, Yixuan Sun, and Jiaqing Li. "Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography." Sensors 24, no. 2 (2024): 333. http://dx.doi.org/10.3390/s24020333.

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This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational
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Dobson, D. C., and F. Santosa. "An image-enhancement technique for electrical impedance tomography." Inverse Problems 10, no. 2 (1994): 317–34. http://dx.doi.org/10.1088/0266-5611/10/2/008.

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Chitturi, Venkatratnam, and Nagi Farrukh. "Narrowband array processing beamforming technique for electrical impedance tomography." Journal of Electrical Bioimpedance 10, no. 1 (2019): 96–102. http://dx.doi.org/10.2478/joeb-2019-0014.

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Abstract Electrical impedance tomography (EIT) has a large potential as a two dimensional imaging technique and is gaining attention among researchers across various fields of engineering. Beamforming techniques stem from the array signal processing field and is used for spatial filtering of array data to evaluate the location of objects. In this work the circular electrodes are treated as an array of sensors and beamforming technique is used to localize the object(s) in an electrical field. The conductivity distributions within a test tank is obtained by an EIT system in terms of electrode vo
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Dimas, Christos, Vassilis Alimisis, Ioannis Georgakopoulos, Nikolaos Voudoukis, Nikolaos Uzunoglu, and Paul P. Sotiriadis. "Evaluation of Thoracic Equivalent Multiport Circuits Using an Electrical Impedance Tomography Hardware Simulation Interface." Technologies 9, no. 3 (2021): 58. http://dx.doi.org/10.3390/technologies9030058.

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Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good
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Huang, Cunkui, Jinhee Lee, William W. Schultz, and Steven L. Ceccio. "Singularity image method for electrical impedance tomography of bubbly flows." Inverse Problems 19, no. 4 (2003): 919–31. http://dx.doi.org/10.1088/0266-5611/19/4/309.

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Stefaniak, Barbara, Tomasz Rymarczyk, Dariusz Wójcik, et al. "Energy Efficiency in Measurement and Image Reconstruction Processes in Electrical Impedance Tomography." Energies 17, no. 23 (2024): 5828. http://dx.doi.org/10.3390/en17235828.

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This paper presents an energy optimization approach to applying electrical impedance tomography (EIT) for medical diagnostics, particularly in detecting lung diseases. The designed Lung Electrical Tomography System (LETS) incorporates 102 electrodes and advanced image reconstruction algorithms. Energy efficiency is achieved through the use of modern electronic components and high-efficiency DC/DC converters that reduce the size and weight of the device without the need for additional cooling. Special attention is given to minimizing energy consumption during electromagnetic measurements and da
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Zhao, Shuanfeng, Yao Miao, Rongxia Chai, et al. "High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials." Advances in Materials Science and Engineering 2022 (March 21, 2022): 1–16. http://dx.doi.org/10.1155/2022/3611691.

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Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal cond
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Saha, Nabanita, and Mohammad Anisur Rahaman. "Bone cancer detection using electrical impedance tomography." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 245. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp245-252.

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Bone cancer is an uncommon sort of malignancy that alludes to irregular development of tissue inside the bone, with high opportunity to spread to different pieces of the body. It is extremely important to distinguish bone cancer at the beginning phase to cure it productively. Presently, in addition to a physical examination, magnetic resonance imaging, blood tests, positron emission tomography (PET), computed tomography (CT) or PET-CT scan, X-ray, Bone scan, Biopsy and computed tomography scan, are used to diagnose or determine the stage (or extent) of bone sarcoma. But these methods are costl
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He, Qing, and Xiao Yan Chen. "EIT Image Processing Based on 2-D Empirical Mode Decomposition." Applied Mechanics and Materials 268-270 (December 2012): 1906–9. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1906.

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Electrical impedance tomography (EIT) imaging is a new kind of imaging technology that has developed in recent years. This technology has many advantages such as noninvasive, small volume and so on, but there are also many shortcomings such as imaging quality is not high, in order to improve the image quality, image processing methods is used in this paper. Significant different with the traditional method, empirical mode decomposition method does not require any prior basis function. It is an adaptive time-frequency analysis method. The two-dimensional empirical mode decomposition algorithm i
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Aleksanyan, Grayr, Nikolay Gorbatenko, Maria Konko, and Cuong Nguyen Manh. "Computer model for acquisition, processing, analysis and visualization of measurement data by electrical impedance tomography." E3S Web of Conferences 389 (2023): 07009. http://dx.doi.org/10.1051/e3sconf/202338907009.

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In this article, a computer model for the collection, processing, analysis and visualization of measurement data by the method of electrical impedance tomography has been developed. The essence of the work performed is to build a computer model of the phantom, electrodes, conductive medium and inhomogeneities in the COMSOL Multiphysics environment to study the conductivity of objects in order to reduce the error in determining the size of the inhomogeneity in the reconstructed image. The study is performed by placing an inhomogeneity phantom in the conductive region. Based on the data obtained
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Dissertations / Theses on the topic "Image processing of Electrical impedance tomography"

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Anderson, Darren Keith. "Theoretical and experimental developments in induced current electrical impedance tomography." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364313.

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Breckon, W. R. "Image reconstruction in Electrical Impedance Tomography." Thesis, Oxford Brookes University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292254.

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Grieve, Bruce Donaldson. "On-line electrical impedance tomography for industrial batch processing." Thesis, University of Manchester, 2002. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:10776.

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This research was originally conceived under the auspices of the UK Government's Foresight Initiative, which aimed to translate the significant body of process tomography knowledge, residing in various British universities, towards applications of generic benefit to industry. In collaboration with the sponsoring life science company, Zeneca Ltd, a number of potential demonstrator projects were identified. Ultimately on-line imaging within pressure filtration was selected by virtue of its direct and broad benefit to the chemical sector and the opportunity to extrapolate the techniques developed
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Kotre, Colin John. "Studies of image reconstruction methods for electrical impedance tomography." Thesis, University of Newcastle Upon Tyne, 1993. http://hdl.handle.net/10443/362.

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Electrical impedance tomography (EIT) is a technique in which images representing the cross-sectional distribution of electrical impedance within a threedimensional object are reconstructed from measurements on the object surface. In this work, some developments of image reconstruction algorithms aimed at increasing the value of this technique in the field of medical diagnosis are studied. The electrical properties of biological tissue and the possibilities for medical applications of EIT are first reviewed. The physical and mathematical basis for EIT is then. examined with particular regard f
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Killingback, Alban Leo Tobias. "Characterisation and data processing for applications of electrical impedance tomography." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389110.

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Soleimani, Manuchehr. "Image and shape reconstruction methods in magnetic induction tomography and electrical impedance tomography." Thesis, University of Manchester, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.512290.

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Zadehkoochak, Mohsen. "Image reconstruction and spectral expansion analysis in electrical impedance tomography." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385528.

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Naidoo, Thoneshan. "Signal and image processing for electrical resistance tomography." Master's thesis, University of Cape Town, 2002. http://hdl.handle.net/11427/5140.

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Bibliography: leaves 139-150.<br>Electrical Resistance Tomography (ERT) is in essence an imaging technique.In ERT current is injected into and removed from a vessel via paired electrodes. The resulting voltage measurements are captured between the remaining electrode pairs. The principle behind ERT is to map these boundary voltages into a conductivity distribution that represents the domain of the vessel. The author has coded a versatile reconstruction algorithm based on the Newton-Raphson algorithm. The knowledge gained by implementing the algorithm is documented in this thesis. The literatur
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Pengpan, Thanyawee. "A combination of motion-compensated cone-beam computed tomography image reconstruction and electrical impedance tomography." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558873.

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Cone-beam computed tomography (CBCT) is an imaging technique used in conjunction with radiation therapy. CBCT is used to verify the position of tumours just prior to radiation treatment session. The accuracy of the radiation treatment of thoracic and upper abdominal tumours is heavily affected by respiratory movement. Blurring artefacts, due to the movement during a CBCT scanning, cause misregistration between the CBCT image and the planning image. There has been growing interest in the use of motion-compensated CBCT for correcting the breathing-induced artefacts. A wide range of iterative rec
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Deba, Charlie Nindjou. "Evaluation and verification of five different image reconstruction algorithms for electrical resistance tomography applications." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2465.

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Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2016.<br>Tomography is the ability to internally visualise an opaque medium or a body, using different imaging techniques. Electrical Resistance Tomography (ERT) technique is a method commonly used in process tomography. It uses a non-intrusive resistance measurement between a set of electrodes attached on the circumference of a fixed cross-section with a given conductivity and permittivity distribution. ERT appears to be simple, low cost, safe and non-invasive. Despite the advantages of ERT, the reconstruction o
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Books on the topic "Image processing of Electrical impedance tomography"

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M, Grigoryan Merughan, ed. Image processing: Tensor transform and discrete tomography with MATLAB. Taylor & Francis, 2012.

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1954-, Woods Richard E., ed. Digital image processing. Addison-Wesley Pub. Co, 1993.

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1954-, Woods Richard E., ed. Digital image processing. 3rd ed. Prentice Hall, 2008.

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1954-, Woods Richard E., ed. Digital image processing. 2nd ed. Prentice Hall, 2002.

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1954-, Woods Richard E., ed. Digital image processing. 2nd ed. Prentice Hall, 2002.

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1954-, Woods Richard E., ed. Digital image processing. Addison-Wesley, 1992.

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Digital Image Processing. Pearson, 2017.

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Digital Image Processing. 3rd ed. Prentice Hall, 2008.

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(Editor), David M. Scott, and Hugh McCann (Editor), eds. Process Imaging For Automatic Control (Electrical and Computer Enginee). CRC, 2005.

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Digital Image Processing: International Edition. Pearson Education, Limited, 2008.

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Book chapters on the topic "Image processing of Electrical impedance tomography"

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Kim, Ho-Chan, Chang-Jin Boo, and Min-Jae Kang. "Image Reconstruction Using Genetic Algorithm in Electrical Impedance Tomography." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_103.

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Pinheiro dos Santos, Wellington, Ricardo Emmanuel de Souza, Reiga Ramalho Ribeiro, et al. "Electrical Impedance Tomography Using Evolutionary Computing: A Review." In Bio-Inspired Computing for Image and Video Processing. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315153797-4.

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Zhao, Zhanqi, Bin Yang, and Lin Yang. "EIT Image Interpretation." In Electrical Impedance Tomography, 2nd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9780429399886-8.

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Lima, Raul Gonzalez, André Luis dos Santos, Erick Dario León Bueno de Camargo, Fernando Silva de Moura, and Talles Batista Rattis Santos. "Signal Processing Architecture for Electrical Tomography Impedance." In IFMBE Proceedings. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-928-8_17.

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Breckon, William, and Michael Pidcock. "Ill-Posedness and Non-Linearity in Electrical Impedance Tomography." In Information Processing in Medical Imaging. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_14.

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Ferreira, Leonardo A., Roberto G. Beraldo, Ricardo Suyama, Fernando S. Moura, and André K. Takahata. "2D Electrical Impedance Tomography Brain Image Reconstruction Using Deep Image Prior." In IFMBE Proceedings. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49404-8_27.

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Boo, Chang-Jin, Ho-Chan Kim, Min-Jae Kang, and Kwang Y. Lee. "Stochastic Optimization Approaches to Image Reconstruction in Electrical Impedance Tomography." In Computational Science and Its Applications – ICCSA 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12165-4_8.

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Wolff, Julia Grasiela Busarello, David William Cordeiro Marcondes, Wellington P. dos Santos, and Pedro Bertemes-Filho. "Image Reconstruction for COVID-19 Using Multifrequency Electrical Impedance Tomography." In Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79753-9_19.

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dos Santos, Wellington Pinheiro, Ricardo Emmanuel de Souza, Valter Augusto de Freitas Barbosa, et al. "Hybrid Metaheuristics Applied to Image Reconstruction for an Electrical Impedance Tomography Prototype." In Hybrid Metaheuristics for Image Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77625-5_9.

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Kim, Ho-Chan, and Chang-Jin Boo. "Intelligent Optimization Algorithm Approach to Image Reconstruction in Electrical Impedance Tomography." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_113.

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Conference papers on the topic "Image processing of Electrical impedance tomography"

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Wang, Kun, Shengwei Teng, Siying Lin, Keyu Chen, and Ruwen Zhao. "An Improved Super-Resolution Image Reconstruction for Electrical Impedance Tomography." In 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). IEEE, 2024. https://doi.org/10.1109/mlnlp63328.2024.10800348.

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Zhu, Hongxi, Dhiya Al-Jumeily, and Panos Liatsis. "A generative approach to Electrical Impedance Tomography image reconstruction using prior information." In 2024 31st International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2024. http://dx.doi.org/10.1109/iwssip62407.2024.10634018.

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Ghoul, Bilel, Bilel Ben Atitallah, Salwa Sahnoun, Ahmed Fakhfakh, and Olfa Kanoun. "Comparative Study of Data Reduction Methods in Electrical Impedance Tomography For Hand Sign Recognition." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10639011.

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Hafsa, Mariem, Kailun Lin, Najoua Essoukri Ben Amara, and Olfa Kanoun. "A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction." In 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2025. https://doi.org/10.1109/ssd64182.2025.10989815.

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Shi, Shuaikai, and Panos Liatsis. "Electrical Impedance Tomography Image Reconstruction Based on Graph Laplacian and Sparse Learning." In 2024 7th International Conference on Power and Energy Applications (ICPEA). IEEE, 2024. https://doi.org/10.1109/icpea63589.2024.10784665.

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Ding, Ye, Zhitian Shen, Weiwei Shao, and Yaoyao Cui. "A Tunable Electrical Impedance Matching System for Dual-Frequency Transducers." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906148.

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Xing, Shiyan, Daqian Yang, and Shuaibin Zhang. "Development of a cuttings migration simulation device based on electrical resistance tomography technology." In Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), edited by Grigorios Beligiannis and Daniel-Ioan Curiac. SPIE, 2024. http://dx.doi.org/10.1117/12.3045781.

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Hou, Weidong, and Yu L. Mo. "New regularized image reconstruction for electrical impedance tomography." In Multispectral Image Processing and Pattern Recognition, edited by Bir Bhanu, Jun Shen, and Tianxu Zhang. SPIE, 2001. http://dx.doi.org/10.1117/12.441539.

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Zehra, Syedah Sadaf, Inam Ul Ahad, Rafay Mehmood Siddiqui, Bassim Aklan, Tahir Uddin, and Zeeshan Islam. "Ventilation Inhomogeneity Analysis using Electrical Impedance Tomography Image Processing." In Biomedical Engineering. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.764-157.

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Yao, Wang, tong zhao, Xiayi Liu, Zhilong Wang, Yiduo Li, and Li Miao. "Detection of multicomponent distribution by electrical impedance spectroscopic tomography." In Twelfth International Conference on Graphics and Image Processing, edited by Zhigeng Pan and Xinhong Hei. SPIE, 2021. http://dx.doi.org/10.1117/12.2589430.

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