Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis

Zobia Suhail, Erika R. E. Denton, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

25 Citations (SciVal)
155 Downloads (Pure)

Abstract

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. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis.

Original languageEnglish
Pages (from-to)1475-1485
Number of pages11
JournalMedical and Biological Engineering and Computing
Volume56
Issue number8
Early online date25 Jan 2018
DOIs
Publication statusPublished - 01 Aug 2018

Keywords

  • Classification
  • Computer aided detection
  • Dimensionality reduction
  • Fisher discriminant analysis
  • Micro-calcification
  • Principal component analysis
  • Humans
  • Support Vector Machine
  • Mammography/methods
  • Databases as Topic
  • Discriminant Analysis
  • Calcinosis/classification
  • Female
  • Principal Component Analysis

Fingerprint

Dive into the research topics of 'Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis'. Together they form a unique fingerprint.

Cite this