A Modified DWT-SVD Algorithm for T1-w Brain MR Images Contrast Enhancement

Abstract Background Image contrast enhancement is considered as the most useful technique permitting a better appearance of the low contrast images. This paper presents a modified Discret Wavelet Transform - Singular Value Decomposition (DWT-SVD) approach for the enhancement of low contrast Brain MR Images used for brain tissues exploration. Methods The proposed technique is processed as follows: first of all, we consider low contrast T1-weighted MRI slices as input images (A1) on which we apply General Histogram Equalization (GHE) algorithm to have equalized images referred as (A2) having zero as mean and one as variance. Secondly, by using the Discrete Wavelet Transform (DWT) algorithm, both (A1) and (A2) are divided into low and high frequency sub-bands. On the low frequency (LL1) and (LL2) sub-bands, SVD is processed in order to generate three matrix factorization (U, V and Σ) where the maximums of (U) and (V) matrix are used for the estimation of a correction coefficient ( ξ ). Our contribution in this paper is to estimate the new singular value matrix (New Σ) using a weighted sum of both original and equalized singular value matrix thanks to an adjustable parameter (μ) for the targeted low contrast images. This parameter, ranged between 0.05 and 0.95, is determined empirically according to the input low contrast image. Finally, the enhanced resulting image is easily reconstructed using the Inverse SVD (ISVD) and the Inverse DWT (IDWT) processes. Results The database considered in our research consisted of 120 MR brain images where T1-weighted MR brain modality are selected for the contrast enhancement process. Considering the qualitative results, our proposed contrast enhancement method have shown better distinction between brain tissues and have preserved all White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF) pixel edges. In fact, histogram plots of images enhanced by proposed method covered all the gray level intensities. For the quantitative results, proposed method gives the highest PSNR, QRCM, SSIM, FSIM and EME values and the lowest AMBE values for (μ) equal to 0.65 as comparing to the rest of methods. These results signifies that proposed contrast enhancement method have provided greater image quality with preservation of image structure, feature and brightness. Conclusion Proposed method improved performance of contrast enhancement image without creating unwanted artifact and without destroying image edge information or affecting the specificities of brain tissues. This is due to the use of an empirically (μ) parameter adjustable according to the input MR images. Hence, the proposed approach is appropriate for enhancing contrast of huge type of low contrast images.