RT Journal Article
T1 Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation
A1 Naseem, Rabia
A1 Khan, Zohaib Amjad
A1 Satpute, Nitin
A1 Beghdadi, Azeddine
A1 Cheikh, Faouzi Alaya
A1 Olivares, Joaquin
K1 Guided enhancement
K1 cross-modality
K1 contrast enhancement
K1 2D histogram specification (HS)
K1 SSIM gradient
K1 tumor segmentation
K1 Adaptive histogram equalization
K1 Brightness
AB 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.
PB Ieee-inst electrical electronics engineers inc
SN 2169-3536
YR 2021
FD 2021-01-01
LK https://hdl.handle.net/10668/28198
UL https://hdl.handle.net/10668/28198
LA en
DS RISalud
RD Apr 11, 2025