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Early Detection of Cancerous Lung Nodules from Computed Tomography Images
SenthilKumar Krishnamurthy, Ganesh EN, Umamaheswari R
Pages - 174 - 187     |    Revised - 31-08-2016     |    Published - 01-10-2016
Volume - 10   Issue - 4    |    Publication Date - October 2016  Table of Contents
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KEYWORDS
Computed Tomography, 3-D Image Segmentation, 3-D Image Features, Volume Growth, Lung Nodule Classifier.
ABSTRACT
This work is developed with an objective of identifying the malignant lung nodules automatically and early with less false positives. ‘Nodule' is the 3mm to 30mm diameter size tissue clusters present inside the lung parenchyma region. Segmenting such a small nodules from consecutive CT scan slices are a challenging task. In our work Auto-seed clustering based segmentation technique is used to segment all the possible nodule candidates. Effective shape and texture features (2D and 3D) were computed to eliminate the false nodule candidates. The change in centroid position of nodule candidates from consecutive slices was used as a measure to eliminate the vessels. The two-stage classifier is used in this work to classify the malignant and benign nodules. First stage rule-based classifier producing 100 % sensitivity, but with high false positive of 12.5 per patient scan. The BPN based ANN classifier is used as the second-stage classifier which reduces a false positive to 2.26 per patient scan with a reasonable sensitivity of 88.8%. The Nodule Volume Growth (NVG) was computed in our work to quantitatively measure the nodules growth between the two scans of the same patient taken at different time interval. Finally, the nodule growth predictive measure was modeled through the features such as tissue deficit, tissue excess, isotropic factor and edge gradient. The overlap of these measures for larger, medium and minimum nodule growth cases are less. Therefore this developed growth prediction model can be used to assist the physicians while taking the decision on the cancerous nature of the lung nodules from an earlier CT scan.
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Mr. SenthilKumar Krishnamurthy
Rajalakshmi Institute of Technology - India
tkseneee@gmail.com
Dr. Ganesh EN
Saveetha Engineering College - India
Dr. Umamaheswari R
Velammal Engineering College - India