dc.contributor.author | Naseem, Rabia | |
dc.contributor.author | Alaya Cheikh, Faouzi | |
dc.contributor.author | Beghdadi, Azeddine | |
dc.contributor.author | Elle, Ole Jakob | |
dc.contributor.author | Lindseth, Frank | |
dc.date.accessioned | 2020-06-30T07:56:38Z | |
dc.date.available | 2020-06-30T07:56:38Z | |
dc.date.created | 2020-01-14T14:07:38Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 2471-8963 | |
dc.identifier.uri | https://hdl.handle.net/11250/2659972 | |
dc.description.abstract | Low contrast Computed Tomographic (CT) images often hamper the diagnosis of critical tumors found in various human organs. Contrast enhancement schemes play significant role in improving the visualization of these structures. To achieve this objective, Crossmodality Guided Enhancement (CMGE) method is proposed in this paper. The idea is to exploit the diversity of the information extracted from one modality to enhance the important structures including vessels and tumors in another modality. Our method employs information from liver Magnetic Resonance Image (MRI) to generate an enhanced CT image. It entails applying two dimensional histogram specification to map 2D histogram of CT to that of MRI followed by application of top and bottom hat transformations. These morphological operations highlight areas brighter than their surroundings and suppress darker areas. The final image is obtained by combining the results of these operations. Our method is compared with other state of the art contrast enhancement methods both visually and in terms of quality assessment metrics IEM and EME. The results show that our method performs better than these methods. CMGE technique yields improved contrast in low contrast CT images of the human liver and highlights tumors and vessels. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.title | Cross modality guided liver image enhancement of CT using MRI | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.journal | European Workshop on Visual Information Processing | en_US |
dc.identifier.doi | 10.1109/EUVIP47703.2019.8946196 | |
dc.identifier.cristin | 1772475 | |
dc.relation.project | EC/H2020/722068 | en_US |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 0 | |