Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection.
This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.
Takemura
Atsushi
A
Institute of Symbiotic Science and Technology, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan. a-take@cc.tuat.ac.jp
Shimizu
Akinobu
A
Hamamoto
Kazuhiko
K
D000465:Algorithms; D001185:Artificial Intelligence; D047688:Breast Cyst / Q000145:classification / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D002277:Carcinoma / Q000145:classification / Q000000981:diagnostic imaging; D016208:Databases, Factual; D005260:Female; D018226:Fibroadenoma / Q000145:classification / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D016217:Ultrasonography, Mammary / Q000379:methods*