Title: Lung nodule growth measurement and prediction using auto cluster seed K-means morphological segmentation and shape variance analysis

Authors: Senthilkumar Krishnamurthy; Ganesh Narasimhan; Umamaheswari Rengasamy

Addresses: Department of ICE, Anna University, Chennai, India ' Department of ECE, Saveetha Engineering College, Anna University, Chennai, India ' Department of EEE, Velammal Engineering College, Anna University, Chennai, India

Abstract: A quantitative model is developed in this work to predict the lung nodules which have the potential to grow in future. An Auto Cluster Seed K-means Morphological segmentation (ACSKMM) algorithm was implemented in this work to segment all the possible lung nodule candidates. An average of around 600 nodule candidates of size >3mm were segmented from each CT scan series of 34 patients. Finally, in total 34 real nodules were remained after eliminating the vessels, non-nodules and calcifications using centroid shift and 3D shape variance analysis. The rate of nodule growth (RNG) was computed on real nodules in terms of 3D-volume change. Out of the 34 real nodules, 3 nodules had RNG value>1, confirming their malignant nature. The nodule growth predictive measure was modelled through compactness, mass deficit, mass excess and isotropic factor.

Keywords: cancer prediction; computed tomography; 3D image segmentation; lung nodule; object shape measurement.

DOI: 10.1504/IJBET.2017.083818

International Journal of Biomedical Engineering and Technology, 2017 Vol.24 No.1, pp.53 - 71

Received: 22 Feb 2016
Accepted: 20 Apr 2016

Published online: 24 Apr 2017 *

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