Next Article in Journal
On Thermodynamic Interpretation of Transfer Entropy
Previous Article in Journal
Energy Potential Mapping: Visualising Energy Characteristics for the Exergetic Optimisation of the Built Environment
Article Menu

Export Article

Open AccessArticle
Entropy 2013, 15(2), 507-523; doi:10.3390/e15020507

Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan Gwagiro, Buk-Gu, Gwangju, 500-712, Korea
*
Author to whom correspondence should be addressed.
Received: 8 November 2012 / Revised: 22 January 2013 / Accepted: 28 January 2013 / Published: 31 January 2013
View Full-Text   |   Download PDF [1276 KB, uploaded 24 February 2015]   |  

Abstract

A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.
Keywords: lung CT; nodule detection; CAD; block classification lung CT; nodule detection; CAD; block classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Choi, W.-J.; Choi, T.-S. Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach. Entropy 2013, 15, 507-523.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top