title: Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses.
abstract: Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Chung-Ming
authors.initials: CM
authors.lastname: Lo
authors.affiliation: Department of Radiology, Seoul National University Hospital, Seoul, Korea. Electronic address: moonwk@snu.ac.kr.
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.affiliation: Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.affiliation: Center for Functional Onco-Imaging and Department of Radiologic Science, University of California at Irvine, Irvine, California, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan.
authors.forename: Jeon-Hor
authors.initials: JH
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Min-Chun
authors.initials: MC
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods / Q000592:standards; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards; D017410:Practice Guidelines as Topic; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D009626:Terminology as Topic; D016217:Ultrasonography, Mammary / Q000379:methods* / Q000592:standards*; D014481:United States; D055815:Young Adult
keywords: Breast cancer; Breast imaging and reporting data system; Computer-aided diagnosis; Ranklet; Ultrasound