3-D ultrasound texture classification using run difference matrix.
Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians' offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run difference matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index A(z) under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively.
Chen
Wei-Ming
WM
Department of Information Management, National Dong Hwa University, Hualien, Taiwan.
Chang
Ruey-Feng
RF
Kuo
Shou-Jen
SJ
Chang
Cheng-Shyong
CS
Moon
Woo Kyung
WK
Chen
Shou-Tung
ST
Chen
Dar-Ren
DR
D019540:Area Under Curve; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*