Multi-set multi-temporal canonical analysis of psoriasis images

David Delgado Gomez, Gabriela Mariel Maletti, Allan Aasbjerg Nielsen, Bjarne Kjær Ersbøll

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    Nowadays, the medical tracking of dermatological diseases is imprecise, mainly due to the lack of suitable objective methods to evaluate the lesion. The severity of the disease is currently scored by doctors merely by means of visual examination. In this work, multi-set canonical correlation analysis over registered images is proposed to track the evolution of the disease automatically. This method transforms the original images into sets of variables that exhibit decreasing degree of similarity, based on correlation measures. Due to this property, these new variables are more suitable to detect where changes occur. An experiment with 5 different time series collected from psoriasis patients during 4 different sessions is conducted. The analysis of the obtained results points out some patterns that can be used both to interpret and summarize the evolution of the lesion and to achieve a better image registration.
    Original languageEnglish
    Title of host publicationIEEE International Symposium on Biomedical Imaging
    Publication date2004
    ISBN (Print)0-7803-8388-5
    Publication statusPublished - 2004
    Event4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, United States
    Duration: 15 Apr 200418 Apr 2004
    Conference number: 4


    Conference4th IEEE International Symposium on Biomedical Imaging
    CountryUnited States
    Internet address

    Bibliographical note

    Copyright: 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE


    • image registration
    • multiset canonical correlation analysis
    • psoriasis

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