title: Robust phase-based texture descriptor for classification of breast ultrasound images.
abstract: BACKGROUND
  Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.
  
  
  METHODS
  The proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.
  
  
  CONCLUSIONS
  The proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It's revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. 12210720095@fudan.edu.cn.
authors.forename: Lingyun
authors.initials: L
authors.lastname: Cai
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. 13210720039@fudan.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. yywang@fudan.edu.cn.|Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China. yywang@fudan.edu.cn.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. guoyi@fudan.edu.cn.|Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China. guoyi@fudan.edu.cn.
authors.forename: Yi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. jhyu@fudan.edu.cn.|Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai, 200433, China. jhyu@fudan.edu.cn.
authors.forename: Jinhua
authors.initials: J
authors.lastname: Yu
authors.affiliation: Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, 200040, China. 09110720016@fudan.edu.cn.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D002277:Carcinoma / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D018226:Fibroadenoma / Q000000981:diagnostic imaging; D006801:Humans; D018300:Papilloma, Intraductal / Q000000981:diagnostic imaging; D055585:Physical Phenomena; D012372:ROC Curve; D012542:Scattering, Radiation; D060388:Support Vector Machine; D016217:Ultrasonography, Mammary