Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis

被引:26
|
作者
Suhail, Zobia [1 ]
Denton, Erika R. E. [2 ]
Zwiggelaar, Reyer [1 ]
机构
[1] Aberystwyth Univ, Aberystwyth, Dyfed, Wales
[2] Norfolk & Norwich Univ Hosp, Norwich, Norfolk, England
关键词
Micro-calcification; Classification; Fisher discriminant analysis; Principal component analysis; Computer aided detection; Dimensionality reduction; MICROCALCIFICATIONS; RECOGNITION;
D O I
10.1007/s11517-017-1774-z
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature.
引用
收藏
页码:1475 / 1485
页数:11
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