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

Authors(4) :-Dr. P. Tamijiselvy, N. Kavitha, K. M. Keerthana, D. Menakha

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 163-168
Manuscript Number : CSEIT195231
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. P. Tamijiselvy, N. Kavitha, K. M. Keerthana, D. Menakha, "An Efficient Clustering Approach for Automatic Detection of Calcification in Low Dose Chest CT", International 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
Journal URL : http://ijsrcseit.com/CSEIT195231

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