Microcalcification Clusters Detection Based on Ensemble Learning

Author

Zhang, Xin-Sheng ; Xie, Hua ; Niu, Ya-Ling

Author_Institution

Sch. of Manage., Xi ´´an Univ. of Arc. & Tech., Xi´´an

Volume

1

fYear

2008

fDate

3-4 Aug. 2008

Firstpage

669

Lastpage

673

Abstract

A new microcalcification clusters (MCs) detection method in mammograms is proposed in this paper, which is based on a new ensemble learning method. The ground truth of MCs is assumed to be known as a priori. In our algorithm, each MCs is enhanced by a well designed high-pass filter. Then the 116 dimensional image features are extracted by the feature extractor and fed to the ensemble decision model. In image feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained ensemble model is used as a classifier to decide the presence of MCs or not. A large number of experiments are carried out to evaluate the proposed MCs detection algorithms. The experimental results illustrate its effectiveness.

Keywords

feature extraction; learning (artificial intelligence); mammography; medical image processing; dimensional image feature extraction; ensemble decision model; ensemble learning; ensemble model training; feature extractor; high-pass filter; image classification; image feature domain; mammograms; microcalcification clusters detection; supervised learning; Bagging; Breast cancer; Cancer detection; Clustering algorithms; Communication system control; Detection algorithms; Feature extraction; Iterative algorithms; Learning systems; Testing; classification; ensemble learning; feature extraction; microcalcification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2008. CCCM '08. ISECS International Colloquium on

Conference_Location

Guangzhou

Print_ISBN

978-0-7695-3290-5

Type

conf

DOI

10.1109/CCCM.2008.311

Filename

4609597