Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images

Soobia Saeed; Habibollah Haron; NZ Jhanjhi; Mehmood Naqvi; Hesham A. Alhumyani; Mehedi Masud; Soobia Saeed; Habibollah Haron; NZ Jhanjhi; Mehmood Naqvi; Hesham A. Alhumyani; Mehedi Masud

doi:10.3934/mbe.2022420

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 9039-9059. doi: 10.3934/mbe.2022420

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Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images

1.
School of Engineering, Faculty of Computing, University Teknologi Malaysia, Skudai 81310, Malaysia
2.
School of Computer Science, SCS Taylor's University, Subang Jaya, Selangor Malaysia
3.
School of Electronics Engineering and Computer Science, Mohwak College, Canada
4.
Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
5.
Department of Computer Science, College of Computer Science and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Academic Editor: Sophia Jang

Received: 22 February 2022 Revised: 15 May 2022 Accepted: 19 May 2022 Published: 22 June 2022

Missing values in the k-NN algorithm are a significant research concern, especially in low-grade tumours and CSF fluid, which are commonly identified in MRI scans. Missing values are usually ignored, but when data is mined, they can lead to bias and errors. In addition, the data is not missing at random. This study improves image accuracy, boosts the efficiency of missing k-NN hybrid values, and develops a research technique for detecting CSF fluid deposits in brain areas separated from non-tumor areas. We also offer a new method for detecting low-grade tumours or cerebrospinal fluid (CSF) formation in its early stages. In this study, we combine the hybrid K-Nearest Neighbor algorithm with the Discrete Fourier transform (DFT), as well as Time-Lagged analysis of four-dimensional (4D) MRI images. These dependencies exist in both space and time, but present techniques do not account for both sequential linkages and numerous types of missingness. To address this, we propose the DFLk-NN imputation method, which combines two imputation approaches based on a hybrid k-NN extension and the DFT to capture time-lag correlations both within and across variables. There are several types of missingness are enables the imputation of missing values across the variable even when all the data for a given time point is missing. The proposed method gives high accuracies of MRI datasets and retrieves the missing data in the images.
- missing values,
- hybrid k-NN,
- DFT,
- MRI,
- datasets
Citation: Soobia Saeed, Habibollah Haron, NZ Jhanjhi, Mehmood Naqvi, Hesham A. Alhumyani, Mehedi Masud. Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9039-9059. doi: 10.3934/mbe.2022420

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Abstract

Missing values in the k-NN algorithm are a significant research concern, especially in low-grade tumours and CSF fluid, which are commonly identified in MRI scans. Missing values are usually ignored, but when data is mined, they can lead to bias and errors. In addition, the data is not missing at random. This study improves image accuracy, boosts the efficiency of missing k-NN hybrid values, and develops a research technique for detecting CSF fluid deposits in brain areas separated from non-tumor areas. We also offer a new method for detecting low-grade tumours or cerebrospinal fluid (CSF) formation in its early stages. In this study, we combine the hybrid K-Nearest Neighbor algorithm with the Discrete Fourier transform (DFT), as well as Time-Lagged analysis of four-dimensional (4D) MRI images. These dependencies exist in both space and time, but present techniques do not account for both sequential linkages and numerous types of missingness. To address this, we propose the DFLk-NN imputation method, which combines two imputation approaches based on a hybrid k-NN extension and the DFT to capture time-lag correlations both within and across variables. There are several types of missingness are enables the imputation of missing values across the variable even when all the data for a given time point is missing. The proposed method gives high accuracies of MRI datasets and retrieves the missing data in the images.

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