Volume 70, Issue 4 (5 2012)                   Tehran Univ Med J 2012, 70(4): 250-256 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

M P H, H S, Sh A, A J, M B K. Designing a new CAD system for pulmonary nodule detection in High Resolution Computed Tomography (HRCT) images. Tehran Univ Med J 2012; 70 (4) :250-256
URL: http://tumj.tums.ac.ir/article-1-126-en.html
1- , mp.hosseini@ymail.com
Abstract:   (7824 Views)

Background: Lung diseases and lung cancer are among the most dangerous diseases with high mortality in both men and women. Lung nodules are abnormal pulmonary masses and are among major lung symptoms. A Computer Aided Diagnosis (CAD) system may play an important role in accurate and early detection of lung nodules. This article presents a new CAD system for lung nodule detection from chest computed tomography (CT) images.

Methods: Twenty-five adult patients with lung nodules in their CT scan images presented to the National Research Institute of Tuberculosis and Lung Disease, Masih Daneshvari Hospital, Tehran, Iran in 2011-2012 were enrolled in the study. The patients were randomly assigned into two experimental (9 female, 6 male, mean age 43±5.63 yrs) and control (6 female, 4 male, mean age 39±4.91 yrs) groups. A fully-automatic method was developed for detecting lung nodules by employing medical image processing and analysis and statistical pattern recognition algorithms.

Results: Using segmentation methods, the lung parenchyma was extracted from 2-D CT images. Then, candidate regions were labeled in pseudo-color images. In the next step, some features of lung nodules were extracted. Finally, an artificial feed forward neural network was used for classification of nodules.

Conclusion: Considering the complexity and different shapes of lung nodules and large number of CT images to evaluate, finding lung nodules are difficult and time consuming for physicians and include human error. Experimental results showed the accuracy of the proposed method to be appropriate (P<0.05) for lung nodule detection.

Full-Text [PDF 4882 kb]   (9034 Downloads)    

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2023 , Tehran University of Medical Sciences, CC BY-NC 4.0

Designed & Developed by : Yektaweb