Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal Analysis

Document Type : Research Articles

Authors

1 Department of Information Technology, Velammal Engineering College, Chennai, India.

2 Department of ECE, Velalar College of Engineering and Technology India.

3 Department of Computer Science and Engineering, Velammal Engineering College, Chennai, India.

Abstract

This paper addresses the automated detection of microcalcification clusters from mammogram images by enhanced
preprocessing operations on digital mammograms for automated extraction of breast tissue from background, removing
artefacts occurring during image registration using X-rays, followed by fractal analysis of suspicious regions.
Identification of breast of either left or right and realigning them to a standard position forms a primitive step in
preprocessing of mammograms. As the next step in the process, pectoral muscles are separated. Suspicious regions of
microcalcifications are identified and are subjected to further analysis of classifying it as benign or malignant. Texture
features are representative of its malignancy and fractal analysis was carried out on extracted suspicious regions for
its texture features. Principal Component Analysis was carried out to extract optimal features. Ten features were
found to be an optimal number of reduced texture features without compromising on classification accuracy. Scaled
conjugate Gradient Back propagation network was used for classification using reduced texture features obtained from
PCA analysis. By varying hidden layer neurons, accuracy of results achieved by proposed methods is analysed and
is calculated to reach maximum accuracy with an optimal level of 15 neurons. Accuracy of 96.3% was achieved with
10 fractal features as input to neural network and 15 hidden layer neurons in neural network designed. The design of
architecture is finalised with maximised accuracy for labelling microcalcification clusters as benign or malignant.

Highlights

 

Keywords

Main Subjects

Full Text

 

References

 
Asian Pacific Journal of Cancer Prevention
Volume 19, Issue 11
November 2018
Pages 3093-3098

Files

History

  • Receive Date: 13 December 2017
  • Revise Date: 06 September 2018
  • Accept Date: 08 October 2018

Share

How to cite

Statistics