Next Article in Journal
Editorial: Special Issue on Efficient Data Structures
Previous Article in Journal
An Enhanced Lightning Attachment Procedure Optimization Algorithm
Article Menu

Export Article

Open AccessArticle

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

1
College of Engineering, Huaqiao University, Quanzhou 362021, China
2
School of medical, Huaqiao University, Quanzhou 362021, China
3
Fujian Provincial Big Data Research Institute of Intelligent Manufacturing, Huaqiao University, Quanzhou 362021, China
4
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(7), 135; https://doi.org/10.3390/a12070135
Received: 26 April 2019 / Revised: 15 June 2019 / Accepted: 27 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Algorithms for Computer-Aided Design)
  |  
PDF [1934 KB, uploaded 3 July 2019]
  |  

Abstract

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value. View Full-Text
Keywords: computer-aided diagnosis; mammography; Contourlet; adaptive support vector machine; classifier computer-aided diagnosis; mammography; Contourlet; adaptive support vector machine; classifier
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Cai, S.; Liu, P.-Z.; Luo, Y.-M.; Du, Y.-Z.; Tang, J.-N. Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM. Algorithms 2019, 12, 135.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top