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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 597313, 11 pages
http://dx.doi.org/10.1155/2015/597313
Research Article

Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm

1College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
2Sansom Institute for Health Research and School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA 5001, Australia
3School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia

Received 2 August 2014; Revised 26 August 2014; Accepted 13 September 2014

Academic Editor: Yi Gao

Copyright © 2015 Liwei Liu et al. 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.

Abstract

A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.