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  • br Swinnen JV Brusselmans K Verhoeven

    2022-05-23


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    190 - Clinical Genitourinary Cancer June 2019  Journal Pre-proof
    Applying a New Quantitative Image Analysis Scheme based on Global Mammographic Features to Assist Diagnosis of Breast Cancer
    Xuxin Chen , Abolfazl Zargari , Alan B Hollingsworth , Hong Liu , Bin Zheng , Yuchen Qiu
    PII:
    DOI:
    Reference:
    To appear in:
    Computer Methods and Programs in Biomedicine
    Please cite this article as: Xuxin Chen , Abolfazl Zargari , Alan B Hollingsworth , Hong Liu ,
    Bin Zheng , Yuchen Qiu , Applying a New Quantitative Image Analysis Scheme based on Global
    This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before Nuclear matrix is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
    Highlights
    · A novel image marker is developed for predicting the malignant lesion depicted on digital mammograms
    · 59 features are extracted from the whole breast area to generate the marker
    · We initially demonstrate that the new marker enables to effectively distinguish the benign and malignant lesions
    Applying a New Quantitative Image Analysis Scheme based on Global Mammographic Features to Assist Diagnosis of Breast Cancer
    Xuxin Chen1†, Abolfazl Zargari1†, Alan B Hollingsworth2, Hong Liu1, Bin Zheng1, Yuchen Qiu1* 1 School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019
    2Department of Surgery, Mercy Health Center, Oklahoma City, OK 73120, United States of America
    * Corresponding Author: Yuchen Qiu,
    School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019. E-mail: [email protected]
    †These authors contributed equally to this work
    Abstract
    Background and Objective: This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer.