Optimized cross-modality guided contrast enhancement for liver tumor segmentation

Abstract

Radiologists currently use CT images with intravenous contrast infusion, in order to detect lesions and vessels in the liver. This step is quite time consuming when it is done manually. There are many algorithms developed for segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). Traditional enhancement methods suffer from limitations such as saturation, over-enhancement, and uneven contrast spatial distribution that may result from the uncontrolled CE process. One way to overcome such limitations is to combine the contrast enhancement approach with a quality control scheme. One way to overcome such limitations is to combine the contrast enhancement approach with a quality control scheme. Inspired by the guided filtering approach and the simplicity of context-aware histogram-based image quality enhancement, propose in this paper a cross-modality guided histogram specification technique to improve the contrast of liver CT images using MRI images as guiding input data. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. 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—: Cross-modality, contrast enhancement, 2D histogram specification (HS), SSIM gradient, tumor segmentation.