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. In this paper, we propose a goal-oriented contrast enhancement method to improve tumor segmentation performance. 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 in a two-step process. The first step consists of applying a two-dimensional histogram specification exploiting contextual information in the corresponding guidance image, i.e. Magnetic Resonance Image (MRI). In the second step, an optimization scheme using a structural similarity measure to preserve the structural information of the original image is performed. To the best of our knowledge, this kind of contrast enhancement optimization scheme using cross-modal guidance is proposed for the first time in the medical imaging context. The experimental results obtained on real data demonstrate the effectiveness of the method in terms of enhancement and segmentation quality in comparison to some state-of-the-art methods based on the histogram.
Highlights
Liver cancer is the fifth most prevalent cancer in the world, carrying a low survival rate [1]
The proposed technique adopts a context-aware 2D histogram-based scheme of exploiting information in the better perceptual quality guidance image for global contrast enhancement, while local image structures are enhanced through structural similarity index measure (SSIM) based measure in an optimization framework
This combination effectively improves the contrast while minimizing the artifacts associated with typical histogrambased enhancement methods to preserve the morphological information of the image during enhancement
Summary
Liver cancer is the fifth most prevalent cancer in the world, carrying a low survival rate [1]. Timely detection of cancerous tumors and effective treatment strategies can improve the overall survival rate Diagnostic imaging techniques such as CT facilitate timely diagnosis of cancer; low contrast and noise limit their utility [2]. It is worth mentioning here that a single medical imaging modality is unable to capture all the relevant structural information from the organs For this reason, it is becoming more common to acquire both CT and MR images periodically during liver cancer diagnosis and treatment [3]. The concept of enhancing the image from one modality using cross-modal image information is not novel; similar ideas have been successfully applied to natural images [4]–[6] One such approach for liver CT image enhancement using the corresponding MR images was proposed to improve the visibility of tumors and vessels [7]. The cross-modality guided enhancement methods have shown better performance in comparison with the classic single image enhancement methods [8], [9]
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