A Novel Method for Reconstruction of Original Images From Extracted Features Using a Combined Wavelet and Regression-Based Approach
Tugba Ozge Onur, Irem Senyer Yapici
article (Uluslararası, Hakemli) | Yazar(lar): Tugba Ozge Onur, Irem Senyer Yapici | Yıl: 2026 | Dergi/Kurum: INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY | İndeks: WOS.SCI | DOI: 10.1002/ima.70422 | Anahtar Kelimeler: feature extraction, medical imaging, regression-based reconstruction, wavelet — Özet: Featu
Feature extraction techniques such as Wavelet Transforms (WT), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM), Gabor filters, and Local Binary Pattern (LBP) are widely used in medical imaging for dimensionality reduction and enhanced image analysis. However, reconstructing the original image from these extracted features is crucial for interpretability, validation, and ensuring data integrity. This study aims to propose a combined approach for reconstructing images using wavelet coefficients and feature extraction techniques, ultimately improving the accuracy and reliability of image reconstruction in medical imaging applications. The wavelet coefficients are processed through Inverse Wavelet Transform (IWT), while a regression model is employed to map extracted features to image details. The combination of multiple reconstruction models-WHOG, WGLCM, WGabor, and WLBP-are compared based on performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Structural Similarity Index (SSIM). Specifically, WHOG refers to the combination of wavelet coefficients and HOG, WGLCM is the combination of WT and GLCM, WGabor integrates WT and Gabor filters, and WLBP combines wavelet coefficients with LBP. The results from three different imaging modalities (Computed Tomography scan for a breast image, Magnetic Resonance Imaging for a brain image, and X-ray for a chest image) show that LBP-based reconstruction delivers the highest fidelity, making it the most robust method for feature-based image reconstruction.