Three Cuts Method for Identification of COPD

  • Mohammad Parsa Hosseini Mail Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA. AND Medical Image Analysis Laboratory, Radiology and Research Administration Departments, Henry Ford Health System, Detroit, MI, USA.
  • Hamid Soltanian-Zadeh Medical Image Analysis Laboratory, Radiology and Research Administration Departments, Henry Ford Health System, Detroit, MI, USA. AND Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
  • Shahram Akhlaghpoor Department of Radiology, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Keywords:
Air trapping, Chronic Obstructive Pulmonary Disease (COPD), Computer Aided Diagnosis (CAD), Neural Network, Pattern Recognition, X-ray Computed Tomography (CT)

Abstract

Two main forms of COPD (Chronic Obstructive Pulmonary Disease) refer to a group of lung diseases that block airflow and cause a huge degree of human suffering. A new method for identifying and estimating the severity of COPD from three-dimensional (3-D) pulmonary X-ray CT images would be helpful for evaluation of treatment effects and early diagnosing is presented in this paper. This method has five main steps. Firstly, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Secondly, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations of three main cuts which selected by a feed-forward neural network are found based on the inspiratory and expiratory states. Finally, a pattern classifier is used to decide about the disease and its severity. Twenty patients with air-trapping problems and twelve normal adults were enrolled in this study. Based on the results, a mathematical model was developed to relate variations of lung volumes to severity of disease. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the accuracy of our method for right regions were %81.6, %80.5, %87.5, %72.5 and %81.3 respectively. And these parameters for left regions were %90, %83.3, %90, %83.3 and %87.5 respectively. The proposed method may assist radiologists in detection of Asthma and COPD as a computer aided diagnosis (CAD) system.

References

World Health Organization. Global surveillance, prevention and control of chronic respiratory diseases: a comprehensive approach, 2007.

Kasper DL, Braunwald E et al. Disorders of respiratory system, in Harrison’s principls of internal medicine, 16th ed., McGraw-Hill, 2005;2:1547.

COPD Statistical Information. http://www.copdinternational. com/, Retrieved on July 11,2010.

Terasaki H, Fujimoto K, Müller NL, Sadohara J, Uchida1 M, Koga T, Aizawa H, Hayabuchi N. Pulmonary sarcoidosis: comparison of findings of inspiratory and expiratory high-resolution CT and pulmonary function tests between smokers and nonsmokers. AJR 2005;185:333-8.

Akira M, Toyokawa K, Inoue Y, Arai T. Quantitative CT in chronic obstructive pulmonary disease: Inspiratory and Expiratory Assessment. AJR 2009;192:267-72.

Zaporozhan J, Ley S, Eberhardt R, Weinheimer O. Paired Inspiratory/Expiratory Volumetric Thin-Slice CT Scan for Emphysema Analysis Comparison of Different. Chest 2005;128:3212-20.

Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor SH. A= Novel Method for Identification of COPD in Inspiratory and Expiratory States of CT Images. Proceeding of theFirst Middle East Conference on Biomedical Engineering,Sharjah, U.A.E., 2011;22-5.

Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor SH, Behrad A. A new scheme for evaluation of air-trapping in CT images. Proceeding of the 6th Iranian Conference on Machine Vision & Image Processing, Isfahan, Iran, 2010.

Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor SH. Assessing Lung Volumetric Variation to Detect and Stage COPD. Proceeding of the First Middle East Conference on Biomedical Engineering, Sharjah, U.A.E. 2011;22-5.

Hosseini MP, Soltanian-Zadeh H, and Akhlaghpoor SH.Computerized Processing and Analysis of CT Images for Developing a New Criterion in COPD Diagnosis. J Isfahan Med School. Special Issue. Biomedical Engineering 2012;29(174):1-7.

Fain SB, Peterson ET, Sorkness RL, Wenzel S, Castro M, Busse WW. Severe asthma research program phenotyping and quantification of severe Asthma. Imaging Decisions J 2009;13(1):18-24.

Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor SH. Detection and Severity Scoring of Chronic Obstructive Pulmonary Disease Using Volumetric Analysis of Lung CT Images. Iran J Radiol. 2012;9(1):22-7. DOI: 10.5812/iranjradiol.6759.

Brown MS, McNitt-Gray MF, Mankovich NJ, Nicholas J, Goldin JG. Method for segmenting chest CT image data using an anatomical model: Preliminary results, IEEE Trans. Med. Imag 1997;16:828-39.

Chan TF, Vese LA. Active Contours Without Edges. IEEE Transactions on Image Processing, 10(2):266-77.

Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor SH, Jalali A, Bakhshayesh Karam M, “Designing a New CAD System for Pulmonary Nodule Detection in High Resolation Compuetd Tomography (HRCT) Images. TUMJ 2012; 70(4):250-6.

Shiying H, Hoffman EA, Reinhardt JM. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Tran. Medical Imaging 2001;20(6):490-8.

Hu S, Hoffman EA, Reinhardt JM, “Automatic Lung Segmentation for Accurate Quantitation of Volumetric Xray CT Images. IEEE Trans Med Imaging 2001;20(6):490-8.

Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Nakajima Y. Airway dimensions at inspiratory and expiratory multisection CT in chronic obstructive pulmonary disease: correlation with airflow limitation. Radiology 2008;248(3):1042-9.

Kuncheva LI. Combining pattern classifier: Methods and Algorithm. John Wiley Inc., Canada 2005; p.8.

Kobayashi R, Tanaka T, Nakamura H, Shirahanta T, et al, Algorithm of pulmonary emphysema analysis using comparing with expiratory and inspiratory state of CT images in Proc. SICE, 2008;3105-9.

Hosseini MP, Soltanian-Zadeh H, and Akhlaghpoor SH. CAD system for the evaluation of chronic obstructive pulmonary disease on CT Images. TUMJ 2011;68(12):718-25.

How to Cite
1.
Hosseini MP, Soltanian-Zadeh H, Akhlaghpoor S. Three Cuts Method for Identification of COPD. Acta Med Iran. 51(11):771-778.
QRcode
Section
Articles