Accessible Unlicensed Requires Authentication Published by De Gruyter January 8, 2013

Novel method of lung area extraction in chest perfusion computed tomography

Renata Kalicka, Seweryn Lipiński and Maciej Browarczyk

Abstract

Chest perfusion computed tomography (pCT) is a useful technique in the medical diagnosis of how organs function. Perfusion CT scans are used to calculate perfusion parameters. In the case of automated methods of lung perfusion parameters calculation, the prior extraction of the lung area is desired to avoid unnecessary calculation in an area outside the lung cross-section and to avoid wasting time on processing signals of no diagnostic importance. Our new method is designed to extract a lung cross-section from a whole series of chest pCT images. It uses a complete sequence of pCT scans to extract the rough lung contour. Next each scan is processed individually, within the rough contour, to obtain a detailed, individual outline of the lungs. The proposed method and obtained results are presented and compared with methods known in literature.


Corresponding author: Seweryn Lipiński, Faculty of Technical Sciences, Department of Electrical and Power Engineering, University of Warmia and Mazury in Olsztyn, 11 Oczapowskiego St., 10-736 Olsztyn, Poland, Phone: +48-89-523-43-64, Fax: +48-89-523-36-03

References

[1] Alonzi R, Hoskin P. Functional imaging in clinical oncology: magnetic resonance imaging- and computerised tomography-based techniques. Clin Oncol 2006; 18: 555–570.Search in Google Scholar

[2] Batchelor B, Waltz F. Interactive image processing for machine vision. London: Springer-Verlag 1993.Search in Google Scholar

[3] Bellotti R, De Carlo F, Gargano G, et al. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Med Phys 2007; 34: 4901–4910.Search in Google Scholar

[4] Blomley MJ, Coulden R, Bufkin C, Lipton MJ, Dawson P. Contrast bolus dynamic computed tomography for the measurement of solid organ perfusion. Invest Radiol 1993; 28: 72–77.Search in Google Scholar

[5] Cao Y. The promise of dynamic contrast-enhanced imaging in radiation therapy. Semin Radiat Oncol 2011; 21: 147–156.Search in Google Scholar

[6] de Nunzio G, Tommasi E, Agrusti A, et al. Automatic lung segmentation in CT images with accurate handling of the hilar region. J Digit Imaging 2011; 24: 11–27.Search in Google Scholar

[7] Dougherty ER, Lotufo RA. Hands-on morphological image processing. Washington, DC: SPIE Press 2003.Search in Google Scholar

[8] Eichinger M, Heussel C-P, Kauczor H-U, Tiddens H, Puderbach M. Computed tomography and magnetic resonance imaging in cystic fibrosis lung disease. J Magn Reson Imaging 2010; 32: 1370–1378.Search in Google Scholar

[9] Homma N, Shimoyama S, Ishibashi T, Yoshizawa M. Lung area extraction from X-ray CT images for computer-aided diagnosis of pulmonary nodules by using active contour model. WSEAS Trans Inf Sci Appl 2009; 5: 746–755.Search in Google Scholar

[10] Ivanovska T, Hegenscheid K, Laqua R, et al. A fast and accurate automatic lung segmentation and volumetry method for MR data used in epidemiological studies. Comput Med Imaging Graph 2012; 36: 281–293.Search in Google Scholar

[11] Iwanowski M. Morphological methods in digital images processing. Warsaw: Academic Publishing House-Exit 2009.Search in Google Scholar

[12] Kalicka R, Lipiński S. A fast method of separation of the noisy background from the head-cross section in the sequence of MRI scans. Biocybern Biomed Eng 2010; 30: 15–27.Search in Google Scholar

[13] Korfiatis P, Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med Phys 2008; 35: 5290–5302.Search in Google Scholar

[14] Krauss A, Nill S, Oelfke U. The comparative performance of four respiratory motion predictors for real-time tumour tracking. Phys Med Biol 2011; 56: 5303–5317.Search in Google Scholar

[15] Kuziemski K, Pieńkowska J, Słomiński W, et al. Role of quantitative chest perfusion computed tomography in detecting diabetic pulmonary microangiopathy. Diabetes Res Clin Pract 2011; 91: 80–86.Search in Google Scholar

[16] Lassen B, Kuhnigk J-M, Schmidt M, Krass S, Peitgen H-O. Lung and lung lobe segmentation methods at Fraunhofer MEVIS. Fourth International Workshop on Pulmonary Image Analysis 2011: 185–200.Search in Google Scholar

[17] Lee YH, Kwon W, Kim MS, et al. Lung perfusion CT: the differentiation of cavitary mass. Eur J Radiol 2010; 73: 59–65.Search in Google Scholar

[18] Malik AS, Choi T-S. A novel algorithm for segmentation of lung images. Lect Notes Comput Sci 2006; 4345: 346–357.Search in Google Scholar

[19] Memon AN, Mirza AM, Gilani SAM. Limitations of lung segmentation techniques. Lect Notes Electric Eng 2009; 27: 753–766.Search in Google Scholar

[20] Murphy K, van Ginneken B, Klein S, et al. Semi-automatic construction of reference standards for evaluation of image registration. Med Image Anal 2011; 15: 71–84.Search in Google Scholar

[21] Osareh A, Shadgar B. A segmentation method of lung cavities using region aided geometric snakes. J Med Syst 2010; 34: 419–433.Search in Google Scholar

[22] Pheng HS, Shamsuddin SM, Kenji S. Application of intelligent computational models on computed tomography lung images. Int J Adv Soft Comput Appl 2011; 3: 1–15.Search in Google Scholar

[23] Pu J, Roos J, Yi CA, Napel S, Rubin GD, Paik DS. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph 2008; 32: 452–462.Search in Google Scholar

[24] Ray N, Acton ST, Altes T, de Lange EE, Brookeman JR. Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation. IEEE Trans Med Imaging 2003; 22: 189–199.Search in Google Scholar

[25] Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 2004; 13: 146–165.Search in Google Scholar

[26] Shan F, Zhang Z, Xing W, et al. Differentiation between malignant and benign solitary pulmonary nodules: use of volume first-pass perfusion and combined with routine computed tomography. Eur J Radiol 2012; 81: 3598–3605.Search in Google Scholar

[27] Shapiro LG, Stockman G. Computer vision. New Jersey, USA: Prentice-Hall 2001.Search in Google Scholar

[28] Silva JS, Silva A, Santos BS. Lung segmentation methods in X-ray CT images. Proc SIARP 2000; 583–598.Search in Google Scholar

[29] Sluimer I, Niemeijer M, van Ginneken B. Lung field segmentation from thin-slice CT scans in presence of severe pathology. Proc SPIE 2004; 5370: 1447–1455.Search in Google Scholar

[30] Sluimer I, Prokop M, van Ginneken B. Toward automated segmentation of the pathological lung in CT. IEEE Trans Med Imaging 2005; 24: 1025–1038.Search in Google Scholar

[31] Sluimer I, Schilham A, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 2006; 25: 385–405.Search in Google Scholar

[32] Sun S, Bauer C, Beichel R. Robust active shape model based lung segmentation in CT scans. Fourth International Workshop on Pulmonary Image Analysis 2011; 213–224.Search in Google Scholar

[33] van Rikxoort EM, van Ginneken B. Automatic segmentation of the lungs and lobes from thoracic CT scans. Fourth International Workshop on Pulmonary Image Analysis 2011; 261–268.Search in Google Scholar

[34] Vinhais C, Campilho A. Lung parenchyma segmentation from CT images based on material decomposition. Lect Notes Comput Sci 2006; 4142: 624–635.Search in Google Scholar

[35] Wang J, Li F, Qiang Lia Q. Automated segmentation of lungs with severe interstitial lung disease in CT. Med Phys 2009; 36: 4592–4599.Search in Google Scholar

[36] Yushkevich PA, Piven J, Hazlett HC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006; 31: 1116–1128.Search in Google Scholar

[37] Zhou X, Hayashi T, Hara T, et al. Automatic recognition of lung lobes and fissures from multi-slice CT images. Proc SPIE 2004; 5370: 1629–1633.Search in Google Scholar

Received: 2012-11-21
Accepted: 2012-12-10
Published Online: 2013-01-08
Published in Print: 2013-02-01

©2013 by Walter de Gruyter Berlin Boston