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

来源：

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING | 2018年 / 56卷 / 08期

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

页码：1475 / 1485

页数：11

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