{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-the-impact-of-intensity","title":"Evaluating the Impact of Intensity Normalization on MR Image Synthesis","arxiv_id":"1812.04652","date":"2018-12-11","proceeding":null,"authors":["Jacob C. Reinhold","Blake E. Dewey","Aaron Carass","Jerry L. Prince"],"abstract":"Image synthesis learns a transformation from the intensity features of an\ninput image to yield a different tissue contrast of the output image. This\nprocess has been shown to have application in many medical image analysis tasks\nincluding imputation, registration, and segmentation. To carry out synthesis,\nthe intensities of the input images are typically scaled--i.e.,\nnormalized--both in training to learn the transformation and in testing when\napplying the transformation, but it is not presently known what type of input\nscaling is optimal. In this paper, we consider seven different intensity\nnormalization algorithms and three different synthesis methods to evaluate the\nimpact of normalization. Our experiments demonstrate that intensity\nnormalization as a preprocessing step improves the synthesis results across all\ninvestigated synthesis algorithms. Furthermore, we show evidence that suggests\nintensity normalization is vital for successful deep learning-based MR image\nsynthesis.","url_abs":"http://arxiv.org/abs/1812.04652v1","url_pdf":"http://arxiv.org/pdf/1812.04652v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evaluating-the-impact-of-intensity","repo_url":"https://github.com/jcreinhold/intensity-normalization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}