An Efficient Clustering Approach for Automatic Detection of Calcification in Low Dose Chest CT

Authors

  • Dr. P. Tamijiselvy  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore Tamil Nadu, India
  • N. Kavitha  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore Tamil Nadu, India
  • K. M. Keerthana  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore Tamil Nadu, India
  • D. Menakha  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195231

Keywords:

Calcium Scoring, Clustering, low-dose chest CT, lung cancer screening.

Abstract

The degree of aortic calcification has been appeared to be a risk pointer for vascular occasions including cardiovascular events. The created strategy is fully automated data mining algorithm to segment and measure calcification using Low-dose Chest CT in smokers of age 50 to 70 .The identification of subjects with increased cardiovascular risk can be detected by using data mining algorithms. This paper presents a method for automatic detection of coronary artery calcifications in low-dose chest CT scans using effective clustering algorithms with three phases as Pre-Processing, Segmentation and clustering. Fuzzy C Means algorithm provides accuracy of 80.23% demonstrate that Fuzzy C means detects the Cardio Vascular Disease at early stage.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

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How to Cite

[1]
Dr. P. Tamijiselvy, N. Kavitha, K. M. Keerthana, D. Menakha, " An Efficient Clustering Approach for Automatic Detection of Calcification in Low Dose Chest CT, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.163-168, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195231