Automated breast cancer classification using near-infrared optical tomographic images.
An automated procedure for detecting breast cancer using near-infrared (NIR) tomographic images is presented. This classification procedure automatically extracts attributes from three imaging parameters obtained by an NIR imaging system. These parameters include tissue absorption and reduced scattering coefficients, as well as a tissue refractive index obtained by a phase-contrast-based reconstruction approach. A support vector machine (SVM) classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes. The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy of 81.8%, 91.7%, and 88.6% respectively, while the classification sensitivity, specificity, and overall accuracy are 63.6%, 83.3%, and 77.1%, respectively, when only the absorption and scattering attributes are used. Furthermore, the automated classification procedure provides significantly improved specificity and overall accuracy for breast cancer detection compared to those by an experienced technician through visual examination.
Wang
James Z
JZ
Clemson University, School of Computing, Clemson, South Carolina 29634, USA.
Liang
Xiaoping
X
Zhang
Qizhi
Q
Fajardo
Laurie L
LL
Jiang
Huabei
H
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007259:Infrared Rays; D012031:Refractometry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D041622:Tomography, Optical