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, w...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on medical imaging 2010-03, Vol.29 (3), p.598-609
Hauptverfasser:	Takemura, A., Shimizu, A., Hamamoto, K.
Format:	Artikel
Sprache:	eng
Schlagworte:	AdaBoost.M2 Algorithms Artificial Intelligence Breast Breast Cyst - classification Breast Cyst - diagnostic imaging Breast neoplasms Breast Neoplasms - classification Breast Neoplasms - diagnostic imaging breast tumor Breast tumors Cancer Carcinoma - classification Carcinoma - diagnostic imaging Classifiers Databases, Factual differential diagnosis Discrimination Female Fibroadenoma - classification Fibroadenoma - diagnostic imaging Humans Image coding Image Interpretation, Computer-Assisted - methods K-distribution log-compressed K-distribution Malignant tumors Parameter estimation Reproducibility of Results Shape support vector machine (SVM) Support vector machine classification Support vector machines Tumors ultrasonic image Ultrasonic imaging Ultrasonic testing Ultrasonography, Mammary - methods
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!