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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
    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 it 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.
    · 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
    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.
    Methods: From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. Next, for each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training a support vector machine (SVM) classifier to generate a final score for predicting likelihood of the case being malignant. To test the scheme, we assembled a dataset involving 275 patients who had biopsy due to the suspicious findings on mammograms. Among them, 134 are malignant and 141 are benign. A ten-fold cross validation method was used to train and test the scheme.
    Results: The classification performance levels measured by the areas under ROC curves are 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively.
    Conclusions: This study demonstrates feasibility of developing a new global mammographic image feature analysis-based scheme to predict the likelihood of case being malignant without lesion segmentation.
    Keywords: Computer-aided diagnosis (CAD), classification of mammograms, quantitative image feature analysis, support vector machine (SVM), particle swarm optimization (PSO) algorithm.
    1. Introduction
    Since the breast lesions are highly heterogeneous containing the overlapped dense fibro-glandular tissues, reading and interpreting mammograms is a difficult task for radiologists [1, 2]. Accordingly, developing computer-aided detection and diagnosis (CAD) schemes of mammograms have attracted extensive research interest in the recent decades, which aims to provide radiologists a “second opinion” supporting tool in reading and interpreting mammograms [3]. Currently, there are two types of CAD schemes namely, computer-aided detection (CADe) schemes and computer-aided diagnosis (CADx) schemes. The former detects suspicious lesions and determines their locations in mammograms [4], while in contrast the latter makes classification between malignant and benign lesions [5]. Although commercialized CADe systems are currently available and used in the clinical practice, it is in controversy of whether using these schemes can actually improve radiologists’ performance in detecting breast cancer