Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation

Abstract

Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. The proposed method is based on two concepts, namely adaptive gamma correction using DWT-SVD and OPTimized Guided Contrast Enhancement (OPTGCE). In the proposed DWT-SVD scheme, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular value matrix of the LL sub-band image. An enhanced LL component is generated using an adequate correction factor and inverse SVD. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image. Tumor segmentation algorithm is applied on the enhanced images to analyze the performance of the proposed method in facilitating tumor segmentation. The qualitative and quantitative analysis using metrics including entropy, MCCEE, and MIGLCM shows the superiority of the proposed method in comparison with the existing methods that do not include guidance mechanism Keywords—: 2D-DWT, Gamma Correction, SSIM, Gradient.