Tomographic image correction with noise reduction algorithms

This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.

[1]  Steven Kay.  Adaptive detection for unknown noise power spectral densities , 1999, IEEE Trans. Signal Process..

[2]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[3]  Ron Wehrens,et al.  Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences , 2011 .

[4]  Yongmiao Hong,et al.  Adaptive penalized splines for data smoothing , 2017, Comput. Stat. Data Anal..

[5]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[6]  Wei Jiang,et al.  Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. , 2018, ISA transactions.

[7]  Armin Lechleiter,et al.  Newton regularizations for impedance tomography: convergence by local injectivity , 2008 .

[8]  Javier Roales,et al.  Optical Gas Sensing of Ammonia and Amines Based on Protonated Porphyrin/TiO2 Composite Thin Films , 2016, Sensors.

[9]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[10]  Oleg V. Michailovich,et al.  Phase unwrapping for 2-D blind deconvolution of ultrasound images , 2004, IEEE Transactions on Medical Imaging.

[11]  Keith Paulsen,et al.  Statistical estimation of resistance/conductance by electrical impedance tomography measurements , 2004, IEEE Transactions on Medical Imaging.

[12]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[13]  John Edwards.  Oceans Are a Testbed for Signal Processing-Driven Technologies [Special Reports] , 2013, IEEE Signal Processing Magazine.

[14]  Kai Hu,et al.  The complex data denoising in MR images based on the directional extension for the undecimated wavelet transform , 2018, Biomed. Signal Process. Control..

[15]  Brian L. Hughes,et al.  Nonconvexity of the capacity region of the multiple-access arbitrarily varying channel subject to constraints , 1995, IEEE Trans. Inf. Theory.

[16]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[17]  Sylvain Meignen,et al.  Time-Frequency Reassignment and Synchrosqueezing: An Overview , 2013, IEEE Signal Processing Magazine.

[18]  Yannis Papanikolaou,et al.  Denoising Autoencoder Self-Organizing Map (DASOM) , 2018, Neural Networks.

[19]  Tomasz Rymarczyk,et al.  A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings , 2018, Sensors.

[20]  Daniela M. Witten,et al.  An Introduction to Statistical Learning: with Applications in R , 2013 .

[21]  Rahim Alhamzawi,et al.  The Bayesian adaptive lasso regression. , 2018, Mathematical biosciences.

[22]  Hee-Seok Oh,et al.  Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals , 2015, IEEE Transactions on Instrumentation and Measurement.

[23]  Pak-Chung Ching,et al.  On wavelet denoising and its applications to time delay estimation , 1999, IEEE Trans. Signal Process..

[24]  Kwong-Sak Leung,et al.  A Modular Plug-And-Play Sensor System for Urban Air Pollution Monitoring: Design, Implementation and Evaluation , 2017, Sensors.

[25]  Min Xu,et al.  A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation , 2017, Journal of structural biology.

[26]  Jean-Michel Poggi,et al.  Multivariate denoising using wavelets and principal component analysis , 2006, Comput. Stat. Data Anal..

[27]  Cheolsoo Park,et al.  Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.